fenzhantw/curated-list-of-data-study

A curated list of Deep Learning / ML / Causality

Deep Learning Study

A curated list of Deep Learning, Reinforcement Learning, Machine Learning, Data Science, Recommendation, Chatbot

Deep Learning

• Tutorial & Lecture
  • 홍콩 과기대 김성훈 교수님의 모두의 딥러닝
  • Deep Learning Tutorial from Tensorflow Blog
    • 해커가 알려주는 뉴럴 네트워크
    • 해커에게 전해들은 머신러닝 : [Slide]
    • 딥 러닝을 위한 콘볼루션 계산 가이드
  • Andrew Ng's Coursera Machine Learning
  • Stanford - CS231n: Convolutional Neural Networks for Visual Recognition : [Video], [Korean], [Video - Korean], [Korean - KNU]
  • Stanford - CS224n: Deep Learning for Natural Language Processing : [Video]
  • Stanford - Unsupervised Feature Learning and Deep Learning Tutorial
  • Stanford - Tensorflow for Deep Learning Research : [CS20[TensorFlow] Lecture Note]
  • Stanford - Theories of Deep Learning [STATS 385]
  • MIT - 6.S191: Introduction to Deep Learning
  • MIT - 6.S094: Deep Learning for Self-Driving Cars
  • Oxford - Deep NLP 2017 course
  • Deep learning courses at UC Berkeley
  • T81-558:Applications of Deep Neural Networks
  • MILA - DEEP LEARNING AND REINFORCEMENT LEARNING SUMMER SCHOOL 2017 : [Video]
  • Deep Learning and Reinforcement Learning Summer School 2018 : [Video]
  • CS 598 LAZ: Cutting-Edge Trends in Deep Learning and Recognition
  • KAIST Machine Learning Lecture
    • [기계학습, 인공지능, 머신러닝] 한국어 기계 학습 강좌
    • [기계학습, 인공지능, 머신러닝] 한국어 기계 학습 강좌 심화 1
  • Udacity - Deep Learning by Google
  • Python Deep Learning with Keras - Machine Learning Mastery
  • fast.ai - Practical Deep Learning For Coders, Part 1
  • fast.ai - Cutting Edge Deep Learning For Coders, Part 2
  • fast.ai - Introduction to Machine Learning for Coders!
  • fast.ai course korean - korean translation + more examples for fastai course contents
  • Deep Learning for Speech and Language
  • 동국대 홍정모 교수님의 C++로 배우는 딥러닝
  • Enjoy DL
  • Laon People 머신러닝/딥러닝 블로그
  • TensorFlow Slim 실습
  • TensorFlow Workshop
  • TensorFlow Tutorials
  • TensorFlow Tutorial : [Video]
  • Machine Learning & Deep Learning
  • T아카데미 인공지능을 위한 머신러닝 알고리즘 강의
  • Deep Learning course: lecture slides and lab notebooks - Master Datascience Paris Saclay
  • Learning Tensorflow - Beginner-level tutorials for a powerful framework
  • Tensorflow for Deep Learning : [Video]
  • 텐서플로우 기초 이해하기
  • Effective Tensorflow
  • Introduction to Deep Neural Networks with Keras and Tensorflow
  • PyTorch로 시작하는 딥러닝 입문 CAMP 1기 강의자료
  • 패스트캠퍼스 Deep Learning 강의 자료
  • 딥러닝 교육 자료
  • Keras 강의 - CodeOnWeb
    • Keras와 함께 딥러닝 백지부터 GANs까지
    • Keras 레퍼런스
  • DeepSchool.io - Deep Learning tutorials in jupyter notebooks
  • Deep Learning Course - PyTorch
  • TensorFlow Tutorial and Examples for Beginners with Latest APIs
  • PyTorch Zero To All
  • FastCampus Deep Learning NLP Chatbot
  • 최신 논문으로 시작하는 딥러닝 - 최성준님 : [Code]
  • Everybody Tensorflow
  • 이찬우님의 패스트 캠퍼스 TensorFlow 딥러닝 강의자료
    • 1. Machine Learning Basic, Linear Regression, Logistic Regression
    • 2. Feed Forward Neural Network
    • 3. Pipeline, TFRecord, Queue Runners, Dataset Framework
    • 4. Convolutional Neural Network
    • 5. Recurrent Neural Network
    • 6. RNN Cells, Advanced RNNs
    • 7. High Level APIs, Estimator, Experiment
    • 8. Word2vec, GAN Basic
  • 딥러닝 이론에서 실습까지 - 엑셈
  • Easy-deep-learning-with-Keras
  • AI Student Kits - Intel Academy
  • Kaggle - Hands-On Data Science Education
  • Google - SuperComputing 2017 Deep Learning Tutorial
  • Google - Machine Learning Crash Course with TensorFlow APIs
  • Google - Machine Learning Practica
  • Google - Machine Learning Tech Dev Guide
  • Lecture Slides for Deeplearning book
  • Microsoft Professional Program for Data Science track
  • Microsoft Professional Program for Artificial Intelligence track
  • Edwith - 인공지능을 위한 선형대수
  • Edwith - 머신러닝을 위한 Python
  • Edwith - Bayesian Deep Learning
  • 딥러닝 퀵스타트 : 파이토치편
  • Open Machine Learning Course
  • 텐서플로 강의 - 이찬우님
  • Machine learning in Python with scikit-learn : [Code]
  • Natural Language Processing with PyTorch
  • PyTorch-Deep-Learning-Minicourse : [Video]
  • Joint course of Megvii Inc. and Peking University on Deep Learning
  • Edwith - Statistics 110 : Probability
  • Edwith - 선형대수 with Khan Academy
  • TensorFlow | A Concise Handbook of TensorFlow Eager Execution
  • Interpretable Machine Learning : [번역]
  • Bloomberg ML EDU - FOUNDATIONS OF MACHINE LEARNING
  • Edwith - [2018] 데이터과학 산책
  • YSDA Natural Language Processing Course
  • Edwith - 신경망과 딥러닝
  • Edwith - 심층 신경망 성능 향상시키기
  • Edwith - 머신러닝 프로젝트 구조화하기
  • Edwith - 합성곱 신경망 네트워크 [CNN]
  • DataScience-for-Beginner - 데이터 과학 기초다지기 교재
  • Machine Learning with AWS
  • Dive into Deep Learning : [번역]
  • Edwith - Data Science from MIT
  • 3분 딥러닝 파이토치맛
  • AI Transformation Playbook
  • 딥 러닝을 이용한 자연어 처리 입문
  • 딥 러닝을 이용한 자연어 처리 심화
  • Berkeley - CS 188 | Introduction to Artificial Intelligence
  • 코더들을 위한 실전 딥러닝 강의
  • 2019년 겨울 한동머신러닝캠프 강의 동영상
  • 텐서플로우와 머신러닝으로 시작하는 자연어처리[로지스틱회귀부터 트랜스포머 챗봇까지]
  • Natural Language Processing Tutorial for Deep Learning Researchers
  • Master Datascience Paris Saclay - Deep Learning course
  • 모두의연구소 - NLP bootcamp
  • Harvard - CS109 Data Science
  • 케라스 창시자에게 배우는 딥러닝
  • 딥러닝 홀로서기 [Ideafactory KAIST]
  • Edwith - School of AI : AI for Business
  • 모두를 위한 딥러닝 시즌 2
  • Edwith - School of AI : Deep Learning Live Coding
  • 한권으로 끝내는 파이썬 & 딥러닝
  • 2019 딥러닝 홀로서기 세미나
  • Handbooks and Code Samples for Software Engineers wanting to learn the Keras Machine Learning framework
  • 딥러닝 입문에서 활용까지 케라스(Keras)
  • Machine Learning 정리
  • NLP 101: 딥러닝과 자연어 처리 학습을 위한 자료 저장소
  • Spring 2019 Full Stack Deep Learning Bootcamp
  • Start Here with Computer Vision, Deep Learning, and OpenCV
  • Advanced NLP with spaCy
  • Introduction to NLP - Tutorial for Beginner
  • Learn on Towards Data Science
  • Natural Language Processing Best Practices & Examples
  • 딥러닝을 위한 TensorFlow 2.0
  • 자연어 언어모델 ‘BERT’
  • the-incredible-pytorch
  • The Super Duper NLP Repo
  • Deep Learning Models
  • Deep Learning with PyTorch
  • Full Stack Deep Learning
  • PyTorch로 시작하는 딥 러닝 입문
  • AI For Everyone MOOC 한글번역 - 김형률
  • 한국어 자연어처리 튜토리얼
  • PyTorch Fundamentals - Microsoft
  • Introduction to Machine Learning Interviews Book
  • 자연어처리 특강
  • PyTorch를 활용한 딥러닝 튜토리얼 (Deep Learning Tutorials with PyTorch)
  • Applied ML - Curated papers, articles, and blogs on data science & machine learning in production
  • Efficient Python Tricks and Tools for Data Scientists
  • Data Science for Beginners
  • NYU Deep Learning Spring 2021 (NYU-DLSP21)
  • Large-scale language modeling tutorials with PyTorch
• Community
  • TensorFlow KR Facebook Group
  • AI Korea Facebook Group
  • AI Korea
  • AI Korea Reddit
  • 텐서플로우 블로그
  • Machine Learning Reddit
  • Deep Learning Facebook Group
  • Deep AI Facebook Group
  • 모두의 연구소 커뮤니티 Facebook Group
  • 모두의 연구소
  • KERAS.AI Facebook Group
  • Bigdata Machine Learning Facebook Group
  • Big Data Korea Facebook Group
  • 딥러닝 솔루션 그룹 Facebook Group
  • AI DEV 인공지능 개발자 모임
  • Distill - Machine Learning Research Journal
  • ArxivSanityKr
  • Towards Data Science - Sharing concepts, ideas, and codes.
  • INSIGHT - Your bridge to careers in Data Science and Data Engineering
  • 카카오 AI 매거진
  • HillClimber.ai - a curated machine learning mashup
  • MyBridge - Machine Learning Top 10 Articles For the Past Month
  • Datascience+ - An online community for showcasing R & Python tutorials
  • Data School - Launch a data science career!
  • Papers with Code
  • explained.ai - Deep explanations of machine learning and related topics
  • Weekly Machine Learning Opensource Roundup
  • Keras for Everyone
  • Browse state-of-the-art
  • Deep Learning Drizzle
  • The Learning Machine
  • 매주 15분 투자해서 AI/NLP를 공부하는 방법
  • DNA(Data Network Analysis) NEWS
  • 집현전 NLP 리뷰 모임
• Article
  • Andrej Karpathy's Deep Learning Blog
  • 머신러닝 딥러닝 입문 시 도움 되는 강좌
  • 딥러닝 입문자용 글 모음
  • 딥러닝 공부 방법
  • 딥러닝 공부를 처음 시작 하는 초심자가 꼭 공부 해야 하는 것이 아닌 것
  • Practical seq2seq
  • New York University Deep Learning Natural Language Processing Lecture Note
  • Intro into Keras and Image Classification : [Video]
  • The Black Magic of Deep Learning - Tips and Tricks for the practitioner
  • How a Japanese cucumber farmer is using deep learning and TensorFlow
  • [개앞맵시] 스카이넷도 딥러닝부터
  • Keras 블로그
  • Coding a Deep Neural Network to Steer a Car: Step By Step
  • Torch와 OpenCV를 활용한 실시간 이미지 분류 데모
  • Variational Autoencoders Explained
  • Generative Adversarial Networks (GANs) in 50 lines of code (PyTorch)
  • 이슈카님의 딥러닝 블로그 : CS231n
  • Hama님의 딥러닝 블로그
  • A Machine Learning Craftsmanship Blog
  • DeepLAB - [머신러닝레볼루션] RNN과 LSTM - 쫄지말자 딥러닝
  • DeepMind just published a mind blowing paper: PathNet
  • Deep Learning for Noobs [Part 2] – Hacker Noon
  • MNIST Generative Adversarial Model in Keras
  • Image Recognition in Python with Keras
  • 유재준님의 딥러닝 블로그
  • Food Classification with Deep Learning in Keras / Tensorflow
  • Accelerating Deep Learning with Multiprocess Image Augmentation in Keras
  • Introduction to deep learning for machine vision tasks using Keras
  • The AWS Deep Learning AMI, Now with Ubuntu
  • Intel’s BigDL on Databricks Distributed deep learning on Apache Spark
  • Deep Learning Research Review: Natural Language Processing
  • Getting Started with Tensorflow
  • 최근우님의 딥러닝 블로그
  • 전상혁님의 머신러닝/딥러닝 블로그
  • Gunho Choi님의 딥러닝 큐레이션 리스트
  • nthought님의 딥러닝/데이터마이닝 블로그
  • KH님의 딥러닝 블로그
  • Deep Learning and Machine Learning Guide: Part I
  • Deep Learning and Machine Learning Guide: Part II
  • Deep Learning and Machine Learning Guide: Part III
  • Deep Learning 학습 자료 정리
  • Deep Learning with Keras
  • Activation Function
  • Deep Learning Conference 후기
  • Building an Image Classification Web Application Using VGG-16
  • PREPARING A LARGE-SCALE IMAGE DATASET WITH TENSORFLOW'S TFRECORD FILES
  • Distributed Deep Learning with Apache Spark and Keras
  • 내가 찾은 Deep Learning 공부 최단경로
  • PyTorch MNIST Example
  • CNN 역전파를 이해하는 가장 쉬운 방법
  • Recurrent Neural Network(RNN)과 LSTM
  • Data Science와 TensorFlow Study 정리 : Data Science와 TensorFlow Study Blog
  • Learn TensorFlow and deep learning, without a Ph.D
  • Visualizing parts of Convolutional Neural Networks using Keras and Cats
  • Machine Learning is Fun!
    • Machine Learning is Fun! The world’s easiest introduction to Machine Learning : [Korean]
    • Machine Learning is Fun! Part 2 Using Machine Learning to generate Super Mario Maker levels : [Korean]
    • Machine Learning is Fun! Part 3: Deep Learning and Convolutional Neural Networks : [Korean]
    • Machine Learning is Fun! Part 4: Modern Face Recognition with Deep Learning : [Korean]
    • Machine Learning is Fun Part 5: Language Translation with Deep Learning and the Magic of Sequences : [Korean]
    • Machine Learning is Fun Part 6: How to do Speech Recognition with Deep Learning
    • Machine Learning is Fun Part 7: Abusing Generative Adversarial Networks to Make 8-bit Pixel Art
  • 딥러닝을 이용한 주가 예측
  • 솔라리스의 인공지능 연구실
  • Directory of tutorials and open-source code repositories for working with Keras, the Python deep learning library
  • Using Caffe with your own dataset
  • Sang-Kil Park님의 딥러닝 블로그
    • 합성곱 신경망(CNN) 딥러닝을 이용한 한국어 문장 분류
    • NLP를 위한 딥러닝 가이드
    • 딥러닝 용어 정리
  • Image Classification and Segmentation with Tensorflow and TF-Slim
  • Reuters-21578 text classification with Gensim and Keras
  • How to Set Up a Deep Learning Environment on AWS with Keras/Thean
  • Bumjun Kim님의 딥러닝 블로그
  • Generative Adversarial Networks – Hot Topic in Machine Learning
  • 조대협님의 머신러닝/딥러닝 블로그
  • RNN(Recurrent Neural Network)과 Torch로 발라드곡 작사하기
  • 모두의 딥러닝 강의 정리
  • Arthur Juliani's Deep Learning Blog
  • Tutorial: Optimizing Neural Networks using Keras (Image recognition)
  • A curated list of resources related to NLP (Natural Language Processing) for Korean + NLP resources in Korean
  • 딥러닝과 에스프레소북 그리고 이것저것들
  • Adit Deshpande's Deep Learning Blos
  • Keras Tutorial: The Ultimate Beginner's Guide to Deep Learning in Python
  • LSTM(RNN) 소개
  • 엑소사랑하자 - OpenFace로 우리 오빠들 얼굴 인식하기
  • Deep Learning Papers Reading Roadmap
  • [번역] A Beginner's Guide To Understanding Convolutional Neural Networks
    • Part 1
    • Part 2
  • RNNS IN TENSORFLOW, A PRACTICAL GUIDE AND UNDOCUMENTED FEATURES
  • Image Completion with Deep Learning in TensorFlow
  • DeepLearning Ninja001 - Hello Tensorflow
  • 딥러닝을 처음 시작하는 분들을 위해
  • List of Pycon2016 session related with ML
  • Awesome - Most Cited Deep Learning Papers
  • 테리님의 딥러닝 블로그
  • Machine Learning & Deep Learning Tutorials
  • Deep Learning for Dummies, Carey Nachenberg
  • TensorFlow-v1.0.0 + Keras 설치 (Windows/Linux/macOS)
  • Deep Learning based Detection
  • LSTM 과 ResNet
  • TensorFlow: How to optimise your input pipeline with queues and multi-threading
  • Image denoising with Autoencoder in Keras
  • How to Build an Image Classification Web App With VGG-16
  • Deep Learning Project Workflow
  • [AI기획]경쟁 통해 배우는 인공지능 기술 GAN
  • How these researchers tried something unconventional to come out with a smaller yet better Image Recognition
  • Understanding Neural Networks Through Deep Visualization
  • Picking an optimizer for Style Transfer
  • Deep Learning with Keras on Google Compute Engine
  • Clickbaits Revisited: Deep Learning on Title + Content Features to Tackle Clickbaits
  • 텐서플로우 시작하기
  • Baidu released PaddlePaddle Jupyter notebook
  • ratsgo님의 블로그
  • Faster R-CNN
  • TensorFlow RNN Tutorial
  • Build Your Own Text-to-Speech Applications with Amazon Polly
  • Five video classification methods implemented in Keras and TensorFlow
  • Build a talking, face-recognizing doorbell for about $100
  • Deep Learning for Vision Guided Language And Image Generation
  • 텐서보드 - TensorBoard 시작하기
  • Classifying White Blood Cells With Deep Learning
  • Diving Into Natural Language Processing
  • Deep Learning with Emojis - not Math
  • 겐[GANs]이 혁신할 인공지능 번역 기술
  • 고려대학교 Deep Learning 세미나
    • DSBA Lab Seminar with cs231n @Stanford
    • DSBA Lab Seminar with cs224d @Stanford
  • Awesome-Pytorch-list
  • Artificial Intelligence GitBook
  • Deploy Deep Learning Models on Amazon ECS
  • DeepLAB : [논문반/논문세미나] SEGAN : Speech Enhancement Generative Adversarial Network
  • awesome-deep-vision-web-demo
  • Introducing tf-seq2seq: An Open Source Sequence-to-Sequence Framework in TensorFlow
  • Kaggle DSTL Competition
  • 14 DESIGN PATTERNS TO IMPROVE YOUR CONVOLUTIONAL NEURAL NETWORKS
  • MXNet을 활용한 이미지 분류 앱 개발하기
  • Tensorflow Tutorial 2: image classifier using convolutional neural network
  • Rohan & Lenny #3: Recurrent Neural Networks & LSTMs
  • Awesome-korean-nlp
  • Deep learning for satellite imagery via image segmentation
  • 지능형 한국어 형태소 분석기 - Korean Intelligent Word Identifier
  • Transfer Learning using Keras
  • Agustinus Kristiadi's Blog [GAN]
  • Everything about Self Driving Cars Explained for Non-Engineers
  • Kaggle Data Science Bown 2017 참가기[지능정보기술연구원]
  • The GAN Zoo
  • THE NEURAL NETWORK ZOO
  • Classification datasets results
  • Deeplunch팀의 Kaggle Data Science Bowl 도전기[1] - 케글 도전 팁
  • A Brief History of CNNs in Image Segmentation: From R-CNN to Mask R-CNN
  • Running BigDL, Deep Learning for Apache Spark, on AWS
  • ImageNet: VGGNet, ResNet, Inception, and Xception with Keras
  • TensorFlow: A proposal of good practices for files, folders and models architecture
  • The Modern History of Object Recognition — Infographic
  • Learning Deep Learning with Keras
  • Deep Learning: Language identification using Keras & TensorFlow
  • Deep Learning Papers by task
  • Deep Learning Tutorials for 10 Weeks
  • Keras Tutorial: Deep Learning in Python
  • 2nd place solution for the 2017 national datascience bowl
  • Deep learning for complete beginners: convolutional neural networks with keras
  • Deep Learning으로 학습된 Object Detection Model 에 대해 정리한 Archive
  • Face recognition with Keras and OpenCV
  • Image segmentation with Neural Net
  • GANs - Generative Adversarial Networks
  • Neural networks for algorithmic trading 1.2 — Correct time series forecasting + backtesting
  • 22 must watch talks on Python for Deep Learning, Machine Learning & Data Science - from PyData 2017, Amsterdam
  • 라즈베리파이기반 TensorFlow 사물인식 로봇
  • 라즈베리파이기반 YOLO 사물인식 로봇
  • Deep Learning #3: More on CNNs & Handling Overfitting
  • pyTorch Tutorials
  • fast.ai: How I built a deep learning application to detect invasive species in just 1 day and for $12.60
  • Picasso: A free open-source visualizer for Convolutional Neural Networks
  • Using Machine Learning to Explore Neural Network Architecture
  • Convolutional Methods for Text
  • Applying deep learning to real-world problems
  • Using TensorFlow to build image-to-text application
  • Your tl;dr by an ai: a deep reinforced model for abstractive summarization
  • Practical UseCases of Deep Learning Techniques… Part II
  • Caption this, with TensorFlow
  • Image Segmentation using deconvolution layer in Tensorflow
  • Exploring LSTMs
  • [YOLO DARKNET] 구성 및 설치, 사용방법
  • You can probably use deep learning even if your data isn't that big
  • TensorFlow for Hackers
    • TensorFlow Basics — TensorFlow for Hackers Part I
    • Building a Simple Neural Network — TensorFlow for Hackers Part II
    • Building a Cat Detector using Convolutional Neural Networks — TensorFlow for Hackers Part III
    • Neural Network from Scratch — TensorFlow for Hackers Part IV
    • Making a Predictive Keyboard using Recurrent Neural Networks — TensorFlow for Hackers Part V
    • Human Activity Recognition using LSTMs on Android — TensorFlow for Hackers Part VI
  • Visualizing TensorFlow Graphs in Jupyter Notebooks
  • Safe Crime Prediction
  • A neural approach to relational reasoning
  • Neural Translation of Musical Style
  • RNN을 이용한 한글 자동 띄어쓰기
  • Object detection with neural networks — a simple tutorial using keras
  • GAN by Example using Keras on Tensorflow Backend
  • Supercharge your Computer Vision models with the TensorFlow Object Detection API
  • Stacking Made Easy: An Introduction to StackNet by Competitions Grandmaster Marios Michailidis - KazAnova
  • Generative Adversarial Networks for Beginners
  • Accelerating Deep Learning Research with the Tensor2Tensor Library
  • Building a Real-Time Object Recognition App with Tensorflow and OpenCV
  • How HBO’s Silicon Valley built “Not Hotdog” with mobile TensorFlow, Keras & React Native
  • How to Visualize Your Recurrent Neural Network with Attention in Keras
  • Interpreting neurons in an LSTM network
  • 머신러닝 실습 with Tensorflow
  • Pytorch를 사용한 단 50줄로 코드로 짜보는 GAN
  • DeepMind’s Relational Reasoning Networks — Demystified
  • Artificial Inteligence
  • How to deploy Machine Learning models with TensorFlow. Part 2— containerize it!
  • Predicting the Success of a Reddit Submission with Deep Learning and Keras
  • CycleGAN : Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks - 컨셉
  • Find Distinct People in a Video with Amazon Rekognition
  • TensorFlow Neural Machine Translation Tutorial
  • Galaxy Zoo classification with Keras
  • 김태희의 닮은 꼴도 머신러닝으로 구분할 수 있을까?
  • An end to end implementation of a Machine Learning pipeline
  • Debugging & Visualising training of Neural Network with TensorBoard
  • Deploy Tensorflow Docker Image to AWS ECS
  • Perform sentiment analysis with LSTMs, using TensorFlow
  • Textboxes - 2016 : Image Text Detection 논문 리뷰
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    • Training MXNet — part 3: CIFAR-10 redux
    • Training MXNet — part 4: distributed training
    • Training MXNet — part 5: distributed training, EFS edition
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  • How to write distributed TensorFlow code — with an example on TensorPort
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  • Gradient Trader Part 1: The Surprising Usefulness of Autoencoders
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  • Recurrent Neural Network [RNN] 이해하기
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  • A new kind of pooling layer for faster and sharper convergence
  • Understanding emotions — from Keras to pyTorch
  • TensorFlow Datasets 및 Estimators를 소개합니다.
  • Visualizing your model using TensorBoard
  • Towards data set augmentation with GANs
  • TensorFlow in a Nutshell
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    • Part Two: Hybrid Learning
    • Part Three: All the Models
  • Introducing NNVM Compiler: A New Open End-to-End Compiler for AI Frameworks
  • Vanilla LSTM with numpy
  • Sentiment analysis with Apache MXNet
  • Question answering with TensorFlow
  • Recurrent neural networks and LSTM Tutorial in Python and TensorFlow
  • Serving PyTorch Models on AWS Lambda with Caffe2 & ONNX
  • Behind the Magic: How we built the ARKit Sudoku Solver
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  • D.Voice: 딥러닝 음성 합성 엔진
  • Video Analysis to Detect Suspicious Activity Based on Deep Learning
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  • Introducing Gluon — An Easy-to-Use Programming Interface for Flexible Deep Learning : [번역]
  • Gender Distribution in North Korean Posters
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  • TF-Slim 시작하기
  • Improving Real-Time Object Detection with YOLO
  • How to unit test machine learning code
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  • 레진 데이터 챌린지 2017
  • Distributed training in the cloud: Cloud Machine Learning Engine
  • Object detection with TensorFlow
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  • What is a CapsNet or Capsule Network?
  • Latest Deep Learning OCR with Keras and Supervisely in 15 minutes
  • Machine Learning Meets Fashion
  • [카카오AI리포트]딥러닝과 데이터
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  • 옹쿠님의 Capsule Network 정리
  • NVIDIA DIGITS 알아보기!
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  • TechtreeAI - AI 학습법
  • Dynamic Routing Between Capsules - 캡슐 간 동적 라우팅
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  • Keras + Horovod = Distributed Deep Learning on Steroids
  • Run Deep Learning Frameworks with GPU Instance Types on Amazon EMR
  • [번역] Go와 Tensorflow로 이미지 인식 API 만들기
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  • SuaLab Research Blog - Deep Learning, Computer Vision
  • Grad CAM을 이용한 딥러닝 모형 해석
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  • AWS Contributes to Milestone 1.0 Release of Apache MXNet Including the Addition of a New Model Serving Capability : [번역]
  • Introducing Model Server for Apache MXNet
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  • Building an Automated Image Captioning Application
  • Serverless deep/machine learning in production — the pythonic way
  • TFGAN: A Lightweight Library for Generative Adversarial Networks
  • AWS Lambda에 Tensorflow/Keras 배포하기
  • GCP CloudML
    • CloudML - 개념
    • CloudML - 기본
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  • Fine-tuning Convolutional Neural Network on own data using Keras Tensorflow
  • Backpropagation – Algorithm For Training A Neural Network
  • gradient를 활용한 DNN 해석 방안
  • Building a spam classifier: PySpark+MLLib vs SageMaker+XGBoost
  • CNN을 이용한 얼굴 분류기
  • TensorFlow Lite를 사용한 온디바이스 대화형 모델링에 대해 확인해 보세요
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  • 당근마켓에서 딥러닝 활용하기
  • Deep Learning Inference & Serving Architecture 를 위한 실험 및 고찰 1 - GPU vs CPU
  • Deep Learning Multi Host & Multi GPU Architecture 고찰 및 구성 1
  • Deep Learning Multi Host & Multi GPU Architecture #2 - Keras 를 이용한 Scale Up, Horovod 를 이용한 Scale Out 성능 비교
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  • How to Deploy Deep Learning Models with AWS Lambda and Tensorflow
  • Zero to Hero: Guide to Object Detection using Deep Learning: Faster R-CNN,YOLO,SSD
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  • Build Amazon SageMaker notebooks backed by Spark in Amazon EMR
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  • Neural Machine Translation — Using seq2seq with Keras
  • Turning Design Mockups Into Code With Deep Learning
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  • Ideas for 9th Kaggle TensorFlow Speech Recognition Challenge
  • Freeze Tensorflow models and serve on web
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  • [Deep Learning - GAN] Simple Generative Adversarial Network with MNIST dataset
  • [Keras] A thing you should know about Keras if you plan to train a deep learning model on a large dataset
  • TensorFlow에서 커스텀 Estimator를 만드는 방법에 대해 확인해 보세요
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  • Digging into AWS SageMaker — First Look
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  • fast.ai : the BEST things in life are always FREE
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  • Machine Learning with TensorFlow on Google Cloud Platform: code samples
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  • Deep learning in production with Keras, Redis, Flask, and Apache
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  • Introducing TensorFlow Hub: A Library for Reusable Machine Learning Modules in TensorFlow
  • Introducing TensorFlow.js: Machine Learning in Javascript
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  • [Keras] 케라스로 풀어보는 다변수 입력에 대한 선형회귀 예제 - 나이, 체중에 대한 혈액지방함량 문제 -
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  • Deep Learning With Apache Spark — Part 2
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  • Introduction to Deep Learning with Keras
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  • Keras와 HDF5으로 대용량 데이터 학습하기
  • MXBoard — MXNet Data Visualization
  • MXNet - Keras gets a lightning fast backend!
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  • Transfer Learning in Tensorflow [VGG19 on CIFAR-10]: Part 2 : [Code]
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  • Stanfoard CS231n 2017 요약
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  • Deep Dive into Math Behind Deep Networks - Mysteries of Neural Networks Part I
  • Preventing Deep Neural Network from Overfitting - Mysteries of Neural Networks Part II
  • Let’s code a Neural Network in plain NumPy - Mysteries of Neural Networks Part III
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  • Introduction to Object Detection
  • What is a Generative Adversarial Network?
  • Introduction to Word Embeddings
  • From GAN to WGAN : [번역]
  • Attention? Attention!
  • From Autoencoder to Beta-VAE
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  • How to implement data augmentation
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  • Kaggle Avito Demand Challenge: "Dance with Ensemble" Sharing Thread
  • Announcing fast.ai part 1 now available as Kaggle Kernels
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  • Deep Learning Tutorial to Calculate the Screen Time of Actors in any Video [with Python codes]
  • Deduce the Number of Layers and Neurons for ANN
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  • Introducing TFServe: Simple and easy HTTP server for tensorflow model inference
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  • From Exploration to Production — Bridging the Deployment Gap for Deep Learning [Part 2]
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  • 10 lessons everyone needs to learn from Fast ai pt.1
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  • How Autoencoders Work: Intro and UseCases
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  • How to Use ELMo Word Vectors for Spam Classification
  • Introduction to Image Caption Generation using the Avenger’s Infinity War Characters
  • Transferring Machine Learning Models from PyTorch to Caffe2 and Mobile Using ONNX
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  • MXNet 초보자를 위한 Gluon 한 시간에 뽀개기
  • Google Colaboratory에서 Keras의 백엔드로서 MXNet을 설정하는 방법
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  • Hyper-parameters in Action! Introducing DeepReplay
  • Colab에서 TensorBoard 및 MXBoard를 사용하여 데이터 시각화 하는 방법
  • Nearest Neighbors with Keras and CoreML
  • Practical Text Classification With Python and Keras
  • Training Cutting-Edge Neural Networks with Tensor2Tensor and 10 lines of code
  • Keras or PyTorch as your first deep learning framework
  • Keras vs PyTorch: how to distinguish Aliens vs Predators with transfer learning
  • Building your First Neural Network on a Structured Dataset [using Keras]
  • Image classification from scratch in keras. Beginner friendly, intermediate exciting and expert refreshing
  • OpenCV Face Recognition
  • Back-Propagation is very simple. Who made it Complicated ?
  • The 4 Convolutional Neural Network Models That Can Classify Your Fashion Images
  • Understanding Neural Networks: What, How and Why?
  • Introducing AdaNet: Fast and Flexible AutoML with Learning Guarantees
  • QNNPACK: Open source library for optimized mobile deep learning
  • Serving ML Quickly with TensorFlow Serving and Docker
  • Image Captioning with Keras — “Teaching Computers to describe pictures”
  • Introduction to 1D Convolutional Neural Networks in Keras for Time Sequences
  • Recurrent Neural Networks by Example in Python
  • Transfer Learning using Mobilenet and Keras
  • How to train Keras model x20 times faster with TPU for free
  • Dogs vs Cats is too easy
  • Introduction to Amazon SageMaker Object2Vec
  • Kaggle Competition — Image Classification
  • Variational AutoEncoders for new fruits with Keras and Pytorch
  • BERT – STATE OF THE ART LANGUAGE MODEL FOR NLP
  • Demystify the TensorFlow APIs
  • Einsum에 대해 간략한 정리
  • EINSUM IS ALL YOU NEED - EINSTEIN SUMMATION IN DEEP LEARNING
  • A Comprehensive Hands-on Guide to Transfer Learning with Real-World Applications in Deep Learning
  • Speed up your deep learning language model up to 1000% with the adaptive softmax, Part 1
  • Speed up your deep learning language model up to 1000% with the adaptive softmax, Part 2: Pytorch implementation
  • Deploy your PyTorch model to Production
  • Deploying a Keras Deep Learning Model as a Web Application in Python
  • Variational Autoencoders Explained in Detail
  • Deep learning: the final frontier for signal processing and time series analysis?
  • Forecasting Air Pollution with Recurrent Neural Networks
  • Mask R-CNN with OpenCV
  • Handling Imbalanced Datasets in Deep Learning
  • Using Google Colab for MNIST with fastai v1
  • 이미지 Segmentation 문제와 딥러닝: GCN으로 개 고양이 분류 및 분할하기
  • Learning FastText
  • Transfer Learning with Convolutional Neural Networks in PyTorch
  • Using Tensorflow Object Detection to control first-person shooter games
  • Time Series Forecasting with LSTMs and Prophet : Predict your e-mail workload
  • Deep Learning cheatsheets for Stanford's CS 230
  • Making Your Neural Network Say “I Don’t Know” — Bayesian NNs using Pyro and PyTorch
  • Speed Up your Algorithms Part 1 — PyTorch
  • Speed Up your Algorithms Part 2— Numba
  • Speed Up Your Algorithms Part 3 — Parallel[-ization]
  • Speeding up your Algorithms Part 4— Dask
  • BERT : Pre-training of Deep Bidirectional Transformers for Language Understanding
  • Recognize relatives using deep learning
  • Image Segmentation: Kaggle experience [Part 1 of 2]
  • Dissecting BERT
  • Automated Machine Learning with Auto-Keras
  • 20세기 폭스에서 ML을 사용해 영화 관람객을 예측하는 방법
  • Audio Classification using FastAI and On-the-Fly Frequency Transforms
  • Solving NLP task using Sequence2Sequence model: from Zero to Hero
  • Tutorial on Text Classification [NLP] using ULMFiT and fastai Library in Python
  • 카카오 형태소 분석기[khaiii] 설치와 은전한닢[mecab] 형태소 분석기 비교
  • Deep Transfer Learning for Natural Language Processing — Text Classification with Universal Embeddings
  • Faster R-CNN [object detection] implemented by Keras for custom data from Google’s Open Images Dataset V4
  • Neural Translation Model with Attention
  • [TGNet] 1.택시 수요 예측 모델 연구 동향을 소개합니다
  • [TGNet] 2.택시 수요 예측 모델을 소개합니다
  • Standardizing on Keras: Guidance on High-level APIs in TensorFlow 2.0
  • Keras Hyperparameter Tuning in Google Colab using Hyperas
  • Object detection and tracking in PyTorch
  • Introducing TF-Ranking
  • Setting up fastai on Google Cloud Platform
  • Experiments with Deep Learning
  • Music Genre Classification with Python
  • Open-sourcing PyText for faster NLP development
  • GANs Demystified — What the hell do they learn?
  • Google Landmark Recognition using Transfer Learning
  • Object Detection using Google AI Open Images
  • Doodling with Deep Learning!
  • BERT 톺아보기
  • The Illustrated BERT, ELMo, and co. [How NLP Cracked Transfer Learning]
  • Distributed TensorFlow using Horovod
  • Deconstructing BERT: Distilling 6 Patterns from 100 Million Parameters
  • Deconstructing BERT, Part 2: Visualizing the Inner Workings of Attention
  • 10 Lessons Learned From Participating in Google AI Challenge
  • Multi-class classification with focal loss for imbalanced datasets
  • Machine Learning — Multiclass Classification with Imbalanced Dataset
  • Word2Vec For Phrases — Learning Embeddings For More Than One Word
  • [Text Classification with State of the Art NLP Library — Flair](https://towardsdatascience.com/text-classification-with-state-of-the-art-nlp-library-flair-b541d7add21f
  • Introducing Wav2letter++
  • SlowFast Explained: Dual-mode CNN for Video Understanding
  • [번역]Deploying Keras models using TensorFlow Serving and Flask
  • The General Ideas of Word Embeddings
  • Malaria Detection with FastAI V1
  • 내 중고물품을 분류 해주는 모델 서빙하기[feat. Keras & Flask]
  • MNIST on TPU [Tensor Processing Unit] or GPU using tf.Keras and tf.data.Dataset
  • Understanding and Coding a ResNet in Keras
  • NLP 2018 Highlights
  • BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
  • Audio Classification using FastAI and On-the-Fly Frequency Transforms
  • Build an image classification model from A to Z [with live demo]
  • Understanding Generative Adversarial Networks [GANs]
  • Advanced Keras — Constructing Complex Custom Losses and Metrics
  • Predicting review scores using neural networks — Kaggle University Club Winter’18 Hackathon
  • 헙! 쉽고 간단한 TensorFlow Hub!?
  • How to use TensorFlow Hub with code examples
  • What’s coming in TensorFlow 2.0
  • Introduction to Video Classification
  • Mask R-CNN for Ship Detection & Segmentation
  • Sentiment Classification with Natural Language Processing on LSTM
  • A CNN classifier and a Metric Learning model,1st Place Solution
  • How to Develop a Stacking Ensemble for Deep Learning Neural Networks in Python With Keras
  • Lessons Learned from Kaggle’s Airbus Challenge.
  • An Overview of Categorical Input Handling for Neural Networks
  • Understanding Entity Embeddings and It’s Application
  • Hands on Machine Learning demo: Real time object detection with YOLO V2
  • Reverse Tabular Module of Fast.ai v1
  • “ TIME SERIES ANALYSIS USING NEURAL NETWORK ”
  • Deep Learning Part 1 — fast.ai - Rossman Notebook
  • Sales Prediction: A Deep Learning Approach
  • Deeplearning with Tabular Data: Visualization, Data Processing, Categorical Embeddings
  • Machine Learning — Text Classification, Language Modelling using fast.ai
  • BERT Technology introduced in 3-minutes
  • A Must-Read NLP Tutorial on Neural Machine Translation — The Technique Powering Google Translate
  • Introducing Ludwig, a Code-Free Deep Learning Toolbox
  • Debugging your tensorflow code right [without so many painful mistakes]
  • FastAI Image Classification
  • Effective TensorFlow 2.0: Best Practices and What’s Changed
  • Building NLP Classifiers Cheaply With Transfer Learning and Weak Supervision
  • Sentiment Analysis using LSTM [Step-by-Step Tutorial]
  • TensorFlow + SageMaker = ❤
  • FastAI Multi-label image classification
  • Building a Malaria Classifier with Keras: Background & Implementation
  • Transfer-Learning: Classification of 4 different types of Arctic Dogs using Fast.AI Library
  • Transfer Learning with PyTorch
  • 딥러닝을 활용한 거래량 예측 기능 개선
  • Keras Wafer Classification CNN2D with Augmetation
  • Introducing TensorFlow Datasets
  • Understand how your TensorFlow Model is Making Predictions
  • An Introduction to Image Augmentation using fastai Library
  • MIT Introduction to Deep Learning
  • One neural network, many uses
  • Learning to LSTM
  • Understanding BERT Transformer: Attention isn’t all you need
  • Build your own Robust Deep Learning Environment in Minutes
  • A Gold-Winning Solution Review of Kaggle Humpback Whale Identification Challenge
  • How We Created a Visual Search Engine for Hayneedle.com
  • Ranking Tweets with TensorFlow
  • Introducing TensorFlow Federated
  • Recap of the 2019 TensorFlow Dev Summit
  • KHANRC님의 AutoML 블로그
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  • 2019 TF dev summit(1) - Keynote
  • 2019 TF dev summit(2) - Introducing Tensorflow 2.0 and its high-level APIs
  • 2019 TensorFlow Dev Summit 요약
  • Chapter 3 : 단어 임베딩을 사용하여 텍스트 유사성 계산하기
  • “Microsoft Malware Prediction” and its 9 million machines
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  • How I won the Flipkart ML challenge
  • On learning embeddings for categorical data using Keras
  • Categorizing Airbnb listing descriptions
  • FastAI Image Segmentation
  • Building efficient data pipelines using TensorFlow
  • Illustrated: Efficient Neural Architecture Search
  • Training Deep Neural Networks on a GPU with PyTorch
  • Easy Image Classification with TensorFlow 2.0
  • Evaluating Keras neural network performance using Yellowbrick visualizations
  • The Illustrated Word2vec : [번역]
  • Graduating in GANs: Going from understanding generative adversarial networks to running your own
  • NLP Keras model in browser with TensorFlow.js
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  • Neural Machine Translation With Attention Mechanism
  • Deep learning to identify Malaria cells using CNN on Kaggle
  • How to Automate Tasks on GitHub With Machine Learning for Fun and Profit
  • How to build your first Neural Network to predict house prices with Keras
  • Generating Images with Autoencoders
  • How to Use Test-Time Augmentation to Improve Model Performance for Image Classification
  • Using BERT and CNNs for Russian Troll Detection on Reddit
  • How a team of deep learning newbies came 3rd place in a kaggle contest
  • A Step-by-Step NLP Guide to Learn ELMo for Extracting Features from Text
  • GENERALIZED LANGUAGE MODELS: COVE, ELMO & CROSS-VIEW TRAINING
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  • GENERALIZED LANGUAGE MODELS: BERT & OPENAI GPT-2
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  • Attention mechanism in NLP. From seq2seq + attention to BERT
  • Attention is all you need paper 뽀개기
  • Annotated Transformer KR - Attention Is All You Need
  • With MorphNet, Google Helps You Build Faster and Smaller Neural Networks
  • Visualizing intermediate activation in Convolutional Neural Networks with Keras
  • TensorFlow를 활용한 네이버쇼핑의 상품 카테고리 자동 분류
  • Boost your CNN image classifier performance with progressive resizing in Keras
  • Extreme Rare Event Classification using Autoencoders in Keras
  • Deep learning for specific information extraction from unstructured texts : [번역]
  • 딥러닝으로 동네생활 게시글 필터링하기
  • Understanding PyTorch with an example: a step-by-step tutorial
  • Getting started with TensorFlow 2.0
  • Transfer Learning : Why train when you can finetune?
  • Generative Adversarial Network : DCGAN을 이용한 이미지 생성
  • TCAV: Interpretability Beyond Feature Attribution
  • Introducing TensorFlow Graphics: Computer Graphics Meets Deep Learning
  • Visualizing A Neural Machine Translation Model (Mechanics of Seq2seq Models With Attention) : [번역]
  • The Illustrated Transformer : [번역]
  • 하이퍼튜닝을 손쉽게 - 케라스 튜너
  • Building a Multi-label Text Classifier using BERT and TensorFlow
  • ARIMA/SARIMA vs LSTM with Ensemble learning Insights for Time Series Data
  • INTUITIVELY UNDERSTANDING VARIATIONAL AUTOENCODERS
  • Using Transfer Learning for NLP with Small Data
  • Building Efficient Custom Datasets in PyTorch
  • Comparison of Activation Functions for Deep Neural Networks
  • How to build a State-of-the-Art Conversational AI with Transfer Learning
  • LSTM Autoencoder for Extreme Rare Event Classification in Keras
  • Predicting future medical diagnoses with RNNs using Fast AI API from scratch
  • Introducing FastBert — A simple Deep Learning library for BERT Models
  • Visualising Filters and Feature Maps for Deep Learning
  • Speedup your CNN using Fast Dense Feature Extraction and PyTorch
  • Applying Deep-Learning for fashion e-commerce
  • Pre-training BERT from scratch with cloud TPU
  • [MT-DNN 논문 Review] Multi-Task Deep Neural Networks for Natural Language Understanding
  • To understand LSTM architecture, code a forward pass with just NumPy
  • Entity Embeddings of Categorical Variables
  • “EfficientNet; Improving Accuracy and Efficiency through AutoML and Model Scaling 리뷰”
  • BERT to the rescue!
  • Visualizing Bias in Data using Embedding Projector
  • KorQuAD (Korean Question Answering Dataset) submission guide using PyTorch pretrained BERT
  • Backpropagation And Batch Normalization In FeedForward Neural Networks Explained
  • Pre-trained Word Embeddings or Embedding Layer? — A Dilemma
  • Implementing a Simple Auto-Encoder in Tensorflow
  • Attn: Illustrated Attention : [번역]
  • Predicting Sales - Forecasting the monthly sales with LSTM
  • A Basic NLP Tutorial for News Multiclass Categorization
  • Diving into Google’s Landmark Recognition Kaggle Competition
  • A Gentle Guide to Starting Your NLP Project with AllenNLP
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  • Introducing TF.Text
  • BERT Classifier: Just Another Pytorch Model
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  • A glance into competitive data science: the best practices for computer vision
  • Using Fastai for Image Classification
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  • A fastai/Pytorch implementation of MixMatch
  • PyTorch Hub 톺아보기
  • Data Augmentation by fastai v1
  • How do Transformers Work in NLP? A Guide to the Latest State-of-the-Art Models
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  • Transformer-XL: Attentive Language Models Beyond a Fixed-Length Context(19.06.23)
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  • 실제 코드로 보는 XLNet (Code Review)
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  • https://nbviewer.jupyter.org/github/metamath1/ml-simple-works/blob/master/BP/bp.ipynb
  • Building State-of-the-Art Language Models with BERT
  • Adapters: A Compact and Extensible Transfer Learning Method for NLP
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  • 꼼꼼하고 이해하기 쉬운 XLNet 논문 리뷰
  • Uncovering what neural nets “see” with FlashTorch
  • Deploying PyTorch inference with MXNet Model Server
  • A Comprehensive State-Of-The-Art Image Recognition Tutorial
  • Microsoft makes AI debugging and visualization tool TensorWatch open source
  • Introducing TensorWatch: Microsoft Research New Tool for Debugging Deep Learning Programs
  • TTA(test time augmentation) with 케라스
  • 이제 하이퍼파라미터 튜닝은 케라스 튜너에게 맡기세요
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  • 딥러닝으로 오디오 만나보기(실전편)
  • XLNet — a clever language modeling solution
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  • 카톡 데이터는 어떻게 정제할 수 있을까? - Dialog-BERT 만들기 1편
  • 누가누가 잘하나! 대화체와 합이 잘 맞는 Tokenizer를 찾아보자! - Dialog-BERT 만들기 2편
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  • Introducing MASS – A pre-training method that outperforms BERT and GPT in sequence to sequence language generation tasks
  • How a simple mix of object-oriented programming can sharpen your deep learning prototype
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  • Generative Adversarial Networks - The Story So Far
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  • Attention Is All You Need — Transformer
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  • Advanced image classification - In Class Kaggle challenge
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  • Introducing tf-explain, Interpretability for TensorFlow 2.0
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  • Understanding Fastai's fit_one_cycle method : [번역]
  • What’s up with Deep Learning optimizers since Adam?
  • A Newbie’s Guide to Stochastic Gradient Descent With Restarts
  • BERT 설명 발표 자료
  • Training Neural Nets on Larger Batches: Practical Tips for 1-GPU, Multi-GPU & Distributed setups
  • The Illustrated GPT-2 (Visualizing Transformer Language Models)
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  • Estimating an Optimal Learning Rate For a Deep Neural Network : [번역]
  • Interpreting Deep Learning Models for Computer Vision
  • Tensorflow 2.0 Tutorial on Categorical Features Embedding
  • Deep Learning Performance Cheat Sheet
  • UNet
  • Changing input size of pre-trained models in Keras
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  • Smaller, faster, cheaper, lighter: Introducing DistilBERT, a distilled version of BERT
  • DCGANs — Generating Dog Images with Tensorflow and Keras
  • Vanilla VQA
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  • Introduction to Deep Learning Normalization
  • How to apply data augmentation to deal with unbalanced datasets in 20 lines of code
  • If you know SQL, you probably understand Transformer, BERT and GPT.
  • Introducing Neural Structured Learning in TensorFlow
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  • Using RoBERTa with Fastai for NLP
  • BERT, RoBERTa, DistilBERT, XLNet — which one to use?
  • BERT for dummies — Step by Step Tutorial
  • Activation maps for deep learning models in a few lines of code
  • A Keras Meta Model Served
  • Time2Vec for Time Series features encoding
  • Image Captioning in Python with Keras
  • Understanding Convolution Neural Networks: CNN— the ELI5 way
  • Meet ALBERT: a new ‘Lite BERT’ from Google & Toyota with State of the Art NLP performance and 18x fewer parameters.
  • TensorFlow 2.0 + Keras Overview for Deep Learning Researchers : [번역]
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  • Modeling Bank’s Churn Rate with AdaNet: A Scalable, Flexible Auto-Ensemble Learning Framework
  • Demystifying BERT: The Groundbreaking NLP Framework
  • Attack Toxic Comments Kaggle Competition Using Fast.ai
  • Visualizing Convolution Neural Networks using Pytorch
  • E2E tf.Keras to TFLite to Android
  • How to train a fastai model and run it in the browser
  • Deep-Learning-in-Production
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  • Tensorflow KR 3차 오프라인 모임 후기
  • Tensorflow KR 3차 오프라인 모임 발표자료
  • Building a mixed-data neural network in Keras to predict accident locations
  • Latest Winning Techniques for Kaggle Image Classification with Limited Data
  • Interpretable Machine Learning for Image Classification with LIME
  • Simple Transformers — Introducing The Easiest Way To Use BERT, RoBERTa, XLNet, and XLM
  • What is tf.keras really?
  • Generalization in Neural Networks
  • What is RAPIDS AI?
  • Categorical Embedding and Transfer Learning
  • [ALBERT 논문 Review] ALBERT: A LITE BERT FOR SELF-SUPERVISED LEARNING OF LANGUAGE REPRESENTATIONS
  • Batch Normalization and Dropout in Neural Networks with Pytorch
  • Fantastic and straightforward image classification with fastai library for Pytorch
  • Speeding up BERT
  • Distilling BERT models with spaCy
  • BERT Text Classification in 3 Lines of Code Using Keras
  • GANs from Zero to Hero: Best Resources for Newcomers
  • Interactive Convolutional Neural Network
  • Learning Fine-grained Image Similarity with Deep Ranking 정리
  • How to build an image duplicate finder for your dataset
  • Best Practices for NLP Classification in TensorFlow 2.0
  • How Deep Neural Networks Look for Features in Images? With Keras and Google Colab
  • Neural averaging ensembles for tabular data with TensorFlow 2.0
  • Image similarity using Triplet Loss
  • Image Similarity using Deep Ranking
  • Finetuning BERT with Tensorflow estimators in only a few lines of code
  • Multi-Label Classification using BERT, RoBERTa, XLNet, XLM, and DistilBERT with Simple Transformers
  • Transfer Learning in NLP
  • Building a Deep Image Search Engine using tf.Keras
  • Question Answering with BERT, XLNET, XLM, and DistilBERT using Simple Transformers
  • Similar Images Recommendations using FastAi and Annoy
  • GLUE: 벤치마크를 통해 BERT 이해하기
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  • How To Fine-Tune GPT-2 So You Can Generate Long-Form Creative Writing
  • Video processing pipeline with OpenCV
  • Fake News Classification with BERT
  • Pre-trained language model in any language
  • Time Series Forecasting with LSTMs using TensorFlow 2 and Keras in Python
  • Introducing the TFX interactive notebook
  • Lit BERT: NLP Transfer Learning In 3 Steps
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  • AI x Bookathon | 인공지능을 [수필 쓰는 작가]로 학습시켜보자!
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  • Fine-tuning BERT with Keras and tf.Module
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  • How to Gain State-Of-The-Art Result on Tabular Data with Deep Learning and Embedding Layers
  • Do it for the ‘gram: Instagram-style Caption Generator
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  • THE 7 QUESTIONS YOU NEED TO ASK TO OPERATE DEEP LEARNING INFRASTRUCTURE AT SCALE
  • Multi-Label Image Classification in TensorFlow 2.0
  • Painless Fine-Tuning of BERT in Pytorch
  • How to train YOLOv3 on Google COLAB to detect custom objects (e.g: Gun detection)
  • Horovod를 활용하여 Tensorflow에서 Multi GPU로 학습하기 (BERT Distributed Training 해보기)
  • Reducing your labeled data requirements (2–5x) for Deep Learning: Deep Mind’s new “Contrastive Predictive Coding 2.0”
  • Transformer (Attention Is All You Need) 구현하기 (1/3)
  • Transformer (Attention Is All You Need) 구현하기 (2/3)
  • Transformer (Attention Is All You Need) 구현하기 (3/3)
  • GPT(Generative Pre-Training) 구현하기 (1/2)
  • GPT(Generative Pre-Training) 구현하기 (2/2)
  • BERT(Bidirectional Encoder Representations from Transformers) 구현하기 (1/2)
  • BERT(Bidirectional Encoder Representations from Transformers) 구현하기 (2/2)
  • SimpleRepresentations: BERT, RoBERTa, XLM, XLNet and DistilBERT Features for Any NLP Task
  • Running distributed TensorFlow training with Amazon SageMaker
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  • Recurrent Neural Network and its Application
  • Introduction to GAN
  • 소프트웨어 2.0을 활용한 게임 어뷰징 검출
  • 빠르게 구현하는 RNN
  • Deep learning [Machine learning] tutorial for beginners
  • 여러 컨볼루션 레이어 테크닉과 경량화 기법들
  • Deep Learning for AI [1]
  • Deep Learning for AI [2]
  • Deep Learning for AI [3]
  • [GAN by Hung-yi Lee]Part 1: General introduction of GAN : [Video]
  • [GAN by Hung-yi Lee]Part 2: The application of GAN to speech and text processing : [Video]
  • [GAN by Hung-yi Lee]Part 3: The recent research of my group : [Video]
  • Various seminars on ML/DL
  • 조희철님의 딥러닝 자료
  • 어머! TPU! 이건 꼭 써야해!
  • [한국어] Neural Architecture Search with Reinforcement Learning : [Video]
  • 딥러닝을 활용한 뉴스 메타 태깅
  • 딥러닝을 이용한 얼굴 인식
  • 개발자가 알아두면 좋을 5가지 AWS 인공 지능 깨알 지식 - 윤석찬 [AWS 테크 에반젤리스트]
  • Video-to-Video Synthesis
  • Deep learning application_to_manufacturing
  • 딥러닝계의 블루오션, Apache MXNet 공헌하기 - 윤석찬 [AWS 테크에반젤리스트] 오규삼 [삼성 SDS]
  • Unsupervised Anomaly Detection with Generative Adversarial Networks for Guide Marker Discovery
  • Image-to-Image Translation
  • Bring Your Own Apache MXNet and TensorFlow Scripts to Amazon SageMaker [AIM350] - AWS re:Invent 2018
  • Building, Training, and Deploying fast.ai Models Using Amazon SageMaker [AIM428] - AWS re:Invent 2018
  • 181123 poseest101 devfest_pangyo_jwkang
  • 딥러닝 자연어처리 - RNN에서 BERT까지
  • Tacotron & Wavenet
  • TensorFlow 2: New Era of Developing Deep Learning Models
  • OS 모바일에서 한글 손글씨 인식하기[with Keras]
  • Designing more efficient convolution neural network
  • Sequence to Sequence Learning with Neural Networks
  • Attention is all you need
  • Efficient Training of Bert by Progressively Stacking
  • 사이킷런 해부학
  • Getting Started with TensorFlow 2.0
  • Structuring your first NLP project
  • A Simple Explanation of XLNet
  • PyCon Korea 2019 - 딥러닝 NLP 손쉽게 따라해보기
  • Bag of Tricks for Image Classification with Convolutional Neural Networks (CVPR 2019) Paper Review
  • GAN을 활용한 My handwriting styler : [Code]
  • '나만의' 코퍼스틑 없다? 자연어처리 연구 데이터의 구축, 검증 및 정제에 관하여
  • 자연어 처리 모델의 성능을 높이는 비결 - 임베딩
  • 딥 러닝 자연어 처리를 학습을 위한 파워포인트. (Deep Learning for Natural Language Processing)
  • Autonomous Driving(feat. Deep Learning)
  • More on Transformers: BERT와 친구들
  • 파이썬 날코딩으로 알고 짜는 딥러닝_1장_회귀분석
  • 파이썬 날코딩으로 알고 짜는 딥러닝_2장
  • 파이썬 날코딩으로 알고 짜는 딥러닝_3장
  • 파이썬 날코딩으로 알고 짜는 딥러닝_4장
  • 파이썬 날코딩으로 알고 짜는 딥러닝_5장
  • 파이썬 날코딩으로 알고 짜는 딥러닝_6장
  • 파이썬 날코딩으로 알고 짜는 딥러닝_7장
  • 파이썬 날코딩으로 알고 짜는 딥러닝_8장
  • 파이썬 날코딩으로 알고 짜는 딥러닝_9장
  • 파이썬 날코딩으로 알고 짜는 딥러닝_10장
  • 파이썬 날코딩으로 알고 짜는 딥러닝_11장
  • 파이썬 날코딩으로 알고 짜는 딥러닝_12장
  • 파이썬 날코딩으로 알고 짜는 딥러닝_13장
  • 파이썬 날코딩으로 알고 짜는 딥러닝_14장
  • 파이썬 날코딩으로 알고 짜는 딥러닝_15장
  • The Illustrated Transformer
  • 네트워크 경량화 이모저모 @ 2020 DLD
  • PyCon2020 NLP beginner's BERT challenge
  • ViT (Vision Transformer) Review [CDM]
  • Talks # 4: Sebastien Fischman - Pytorch-TabNet: Beating XGBoost on Tabular Data Using Deep Learning : [Video]
  • 딥 러닝 자연어 처리를 학습을 위한 파워포인트. (Deep Learning for Natural Language Processing)
• Video
  • Andrej Karpathy's Youtube Channel
  • Intro to Deep Learning (Udacity Nanodegree) - Siraj Raval
  • Feeding your own data set into the CNN model in Keras
  • Intro into Image classification using Keras
  • Integrating Keras & TensorFlow: The Keras workflow, expanded
  • 테리님의 딥러닝 토크
  • DeepLearning.TV
  • Deep Learning From A to Z - Raphael Gontijo Lopes
  • 페이스북, AI대가 '얀 레쿤 교수' 인공지능 강의 공개
  • Deep Learning with Keras and Python
  • How Deep Neural Networks Work
  • TensorFlow Tutorial
  • Deep Learning with Python
  • How to Deploy Keras Models to Production
  • Python Plays: Grand Theft Auto V
  • PyDataTV
  • Deep Learning with Tensorflow - Cognitive Class
  • 12인회 논문 읽기 비디오
  • Deep learning with Keras
  • 머신러닝/딥러닝 실전 입문
  • Neural Networks - 3Blue1Brown
  • Deep Learning and Streaming in Apache Spark 2 x - Matei Zaharia & Sue Ann Hong
  • Apache MXNet으로 배워보는 딥러닝
  • 헬로 딥러닝 - 남세동님 : [eBook]
  • 빅데이터, 머신러닝, 그리고 AI
  • AWS의 새로운 통합 딥러닝 서비스, Amazon SageMaker - 김무현 솔루션즈 아키텍트 [AWS]
  • Getting Started With AWS SageMaker
  • AWS SageMaker Deep Learning for Breast Cancer Prediction
  • How To Pull Data into S3 using AWS Sagemaker
  • An overview of Amazon SageMaker
  • Image classification with Amazon SageMaker
  • Deep Learning Practitioner의 캐글 2회 참가기
  • PR-099: MRNet-Product2Vec
  • 주재걸 교수님의 머신러닝/딥러닝/선형대수 강의영상
  • 최성철 교수님의 머신러닝/데이터과학 강의영상
  • TensorFlow, Deep Learning, and Modern Convolutional Neural Nets, Without a PhD [Cloud Next '18]
  • Artificial Intelligence Lecture Series
  • Graph neural networks: Variations and applications
  • 빵형의 개발도상국
  • 트랜스포머 [어텐션 이즈 올 유 니드]
  • Attention (1): What is Attention?
  • Learn Natural Language Processing
  • PR-201: Bag of Tricks for Image Classification with Convolutional Neural Networks
  • Solving NLP Problems with BERT | Yuanhao Wu | Kaggle
  • [통계청 현직 AI] Colab에서 케라스 BERT로 네이버 영화 감성분석 따라하기 Keras Bert implementation on google Colaboratory
  • Subword-level Word Vector Representations for Korean - 주현진
  • [통계청 공무원 AI] BERT로 Q&A 구현해보기 With SQuAD AND KERAS
  • AutoML-Zero
  • 15min Tutorial : keras + CNN + MNIST + Colab
  • NLP for Developers
  • Text Classification | Sentiment Analysis with BERT using huggingface, PyTorch and Python Tutorial
  • DeepMind x UCL | Deep Learning Lecture Series 2020
  • Opening Up the Black Box: Model Understanding with Captum and PyTorch
  • DETR: End-to-End Object Detection with Transformers (꼼꼼한 딥러닝 논문 리뷰와 코드 실습)
  • StarGAN (꼼꼼한 딥러닝 논문 리뷰와 코드 실습)
  • Talks # 13: William Falcon; Stop engineering, start winning - How to Kaggle with PyTorch Lightning
  • PR-281: An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale
  • ResNet: Deep Residual Learning for Image Recognition (꼼꼼한 딥러닝 논문 리뷰와 코드 실습)
  • 3 lines of code conversational AI with NVIDIA NeMo and PyTorch Lightning
  • Illustrated Guide to Transformers Neural Network: A step by step explanation
  • Pytorch Transformers from Scratch (Attention is all you need)
  • [딥러닝 기계 번역] Transformer: Attention Is All You Need (꼼꼼한 딥러닝 논문 리뷰와 코드 실습)
  • GAN: Generative Adversarial Networks (꼼꼼한 딥러닝 논문 리뷰와 코드 실습)
  • Machine Translation Survey (vol1) : Background
  • Deep Learning for Tabular Data: A Bag of Tricks | ODSC 2020
  • Google I/O 2021 Extended Bacolod (Vertex AI) : [Slide]
  • Transformer Survey Study
  • Workshop: Getting started with Amazon Sagemaker Train a Hugging Face Transformers and deploy it : [Code]
  • Deep Compression [꼼꼼한 딥러닝 논문 리뷰와 코드 실습]
  • PR-072: Deep Compression
  • Managed Training with Amazon SageMaker and 🤗 Transformers
  • [메릭 웨비나] 기계학습(머신러닝) 모델의 압축 기술 - 정태희 박사(Xilinx, machine learning acceleration)
  • 가중치 공유를 통한 딥러닝 모델 압축 (이강호 | 경희대학교)
  • Creating a Training Pipeline with PyTorch Lightning and Hydra
• Code
  • Fast PixelCNN++: speedy image generation
  • Keras with Deeplearning4j
  • DeepDream in Keras
  • Neural-Chatbot by Keras
  • Detects Clickbait Headlines Using Deep Learning: Clickbait Detector
  • A self-driving car simulator built with Unity
  • deep-facebook-commenter
  • Sequential model in Keras -> ASCII
  • Deep Q&A
  • TensorFlow Tutorials
  • A toy chatbot powered by deep learning and trained on data from Reddit
  • ML_Practice with TensorFlow
  • Keras-Tutorials
  • Tensorflow Tutorials using Jupyter Notebook
  • Simple implementation of Generative Adversarial Networks
  • Generative Adversarial Network for approximating a 1D Gaussian distribution
  • pytorch-tutorial
  • DeepLearningForNLPInPytorch
  • Building an image classifier using keras
  • Deep Learning for Self-Driving Cars
  • Keras Generative Adversarial Networks
  • DiscoGAN - SKT Brain
  • DiscoGAN in PyTorch
  • DiscoGAN in Tensorflow
  • Variational Auto-Encoder for MNIST
  • Kind PyTorch Tutorial for beginners
  • Distributed Deep Learning on AWS Using MXNet and TensorFlow
  • Keras-GAN-Animeface-Character
  • Object Recognition using TensorFlow and Java
  • Real-time face detection and emotion/gender classification using fer2013/imdb datasets with a keras CNN model and openCV
  • Keras based Neural Style Transfer
  • RNN-implementation-using-Numpy-binary-digit-addition
  • keras implementation of [A simple neural network module for relational reasoning]
  • Building AnswerBot with Keras and Tensorflow
  • Traffic Sign Recognition with Keras
  • Neural image captioning implementation with Keras 2
  • Seq2seq Chatbot for Keras
  • Digit Recognizer with CNN on Keras
  • MXNet Notebooks
  • Textgenrnn - Python module to easily generate text using a pretrained character-based recurrent neural network
  • Mxnet_Tutorial
  • Tensorflow implementation of different GANs and their comparisions
  • An end-to-end tutorial for OCR recognition using CNN
  • Notebook from the Deep Learning Twitch Series on AWS - MXNet
  • Tensorflow implementation of various GANs and VAEs
  • Pytorch implementation of various GANs
  • Chatbot in 200 lines of code
  • Jupyter notebooks for the code samples of the book "Deep Learning with Python"
  • Python package with source code from the course "Creative Applications of Deep Learning w/ TensorFlow"
  • GANs comparison without cherry-picking
  • Simple GAN model using keras with Fashion-mnist data
  • Lambda API to caption images [with im2txt]
  • Multi-layer Recurrent Neural Networks for character-level language models in Python using Tensorflow by 1.3 version [Estimator, Experiment, Dataset]
  • Keras-GAN
  • Demo of running NNs across different deep learning frameworks
  • Distributed TensorFlow Guide
  • Jupyter-Tensorboard: Start tensorboard in Jupyter notebook
  • TensorNets - High level network definitions with pre-trained weights in TensorFlow
  • A neural chatbot using sequence to sequence model with attentional decoder implements by Tensorflow 1.4 version
  • Mask R-CNN for object detection and instance segmentation on Keras and TensorFlow
  • Pytorch implementations of various Deep NLP models in cs-224n[Stanford Univ]
  • Korean Restaurant Reservation Dialogue System
  • 구글 머신러닝 워크샵 교육 코드
    • Day 1
    • Day 2
  • Convolutional Neural Networks for Sentence Classification[TextCNN] implements by TensorFlow
  • imgaug - Image augmentation for machine learning experiments
  • Pytorch easy-to-follow Capsule Network tutorial
  • Understanding NN - Tensorflow tutorial for "Methods for Interpreting and Understanding Deep Neural Networks"
  • Neural Korean to English Machine Translater with Gluon
  • Simple Tensorflow DatasetAPI Tutorial for reading image
  • This repository provides tutorial python scripts used in the EverybodyTensorlfow lecture by Jaewook Kang
  • Unsupervised anomaly detection with generative model, keras implementation
  • GAN in Numpy
  • Deep Learning Study with Gluon
  • Deploy Keras Model with Flask as Web App in 10 Minutes
  • NLP Tutorial with Deep Learning using tensorflow
  • TensorFlow Advanced Tutorials
  • Repo for the Deep Learning Nanodegree Foundations program
  • Experiments with Deep Learning
  • 2018 TF Pattern Design Study in MoT
  • KEKOxTutorial - 전 세계의 멋진 케라스 문서 및 튜토리얼을 한글화하여 케라스x코리아를 널리널리 이롭게합니다
  • tf.data examples for keras and estimator models
  • How to run Object Detection and Segmentation on a Video Fast for Free
  • 3i4K - Intonation-aided intention identification for Korean
  • DLK2NLP: Day-by-day Line-by-line Keras-based Korean NLP
  • Material used for Deep Learning related workshops for Machine Learning Tokyo [MLT]
  • A list of NLP[Natural Language Processing] tutorials
  • PyTorch tutorial for learners
  • CS 20 : TensorFlow for Deep Learning Research
  • NLP Classification Tutorial with PyTorch
  • Classification models trained on ImageNet. Keras.
  • 이찬우님의 패스트캠퍼스 강의용 코드
    • 2. tf.keras API basic
    • 3. Regression modeling
    • 4. FFNN modeling
    • 5. FFNN modeling [MNIST]
    • 6. CNN modeling
    • 7. RNN modeling
  • Tensorflow RNN Tutorial
  • Simple Tensorflow Cookbook for easy-to-use
  • A list of NLP[Natural Language Processing] tutorials
  • cs231n강좌의 백프로파게이션 부분 Numpy구현
  • NLP paper implementation with PyTorch
  • Transformer Encoder with Char information for text classification
  • Natural_language_Processing_self_study
  • Deep Learning Models
  • Repo with all Project types including: "Stanford Cars, road to 90%+"
  • Source code for "Efficient Training of BERT by Progressively Stacking"
  • Stanford Cars Classification using keras and fastai
  • How to serve pretrained models using Clipper
  • Deep learning introduction to beginners with PyTorch
  • OpenNMT Colab Tutorial Pytorch && Tensorflow
  • Keras Optimizer with Gradient Accumulation
  • Codes used on AI Starthon 2019. 1st place in total.
  • Stanford Cars Classification using keras and fastai
  • Korean BERT pre-trained cased (KoBERT)
  • Running your TensorFlow Models in SageMaker Workshop
  • AIHub Dataset + Detectron2 Tutorial
  • Automatic Korean word spacing with TensorFlow 2.0 + Keras
  • Dacon 14th Competition 1st Place- "Financial smishing character analysis"
  • Dacon 14th Competition [euphoria] public 17위 private 10위 코드 공유
  • Best Practices, code samples, and documentation for Computer Vision
  • A list of NLP(Natural Language Processing) tutorials built on PyTorch
  • COVID-19_Classification
  • Tensorflow2 Cookbook
  • The fastai book
  • Detection of Accounting Anomalies using Deep Autoencoder Neural Networks
  • f-AnoGAN: Fast Unsupervised Anomaly detection with GAN using Pytorch
  • NarrativeKoGPT2 - koGPT-2를 이용한 이야기 생성 AI
  • Tutorial for pretraining Korean GPT-2 model
  • Simple Chit-Chat based on KoGPT2
  • KcBERT: Korean comments BERT
  • Text-Classification-Transformers
  • Neural Plot - A Library for visualizing Neural Networks of the TensorFlow/Keras models
  • Disentangling Label Distribution for Long-tailed Visual Recognition
  • PyTorch-StudioGAN
  • KoBART
  • Implementation of the paper Last Query Transformer RNN for knowledge tracing - Last-Query-Transformer-RNN
  • 진짜로(?) 주석 달린 트랜스포머Really annotated transformers†
• Tool
  • TensorFlow - Google
  • Keras - Google
  • Caff2 - Facebook
  • PyTorch - Facebook
  • MXNet - AWS
  • CNTK - Microsoft
  • PaddlePaddle - Baidu
  • Neural Network Libraries - Sony
  • Caffe
  • Theano
  • Torch
  • DeepLearning4J
  • Chainer
  • Kur
  • OpenNMT - An open-source neural machine translation system
  • tf-seq2seq
  • ParlAI - A framework for training and evaluating AI models on a variety of openly available dialog datasets
  • NeuroNER - A Named-Entity Recognition Program based on Neural Networks and Easy to Use
  • spaCy - Industrial-Strength Natural Language Processing
  • Keras Visualization Toolkit
  • Cheat Sheets for AI, Neural Networks, Machine Learning, Deep Learning & Big Data
  • DeepForge - A Modern Development Environment for Deep Learning
  • TensorFire - A framework for running neural networks in the browser, accelerated by WebGL
  • deeplearn.js - A hardware-accelerated machine intelligence library for the web
  • Beholder - A TensorBoard plugin for visualizing arbitrary tensors in a video as your network trains
  • AllenNLP - An open-source NLP research library, built on PyTorch
  • StarSpace - Learning embeddings for classification, retrieval and ranking
  • Fabrik – Collaboratively build, visualize, and design neural nets in the browser : [Code]
  • LUMINOTH - Open source Computer Vision toolkit
  • Horovod - Uber’s Open Source Distributed Deep Learning Framework for TensorFlow
  • Deepo - A Docker image containing almost all popular deep learning frameworks
  • Skorch - A scikit-learn compatible neural network library that wraps PyTorch
  • Kubeflow - Machine Learning Toolkit for Kubernetes
  • Darkon: Toolkit to Hack Your Deep Learning Models
  • Detectron - FAIR's research platform for object detection research, implementing popular algorithms like Mask R-CNN and RetinaNet
  • Visual Search with MXNet Gluon
  • AutoKeras - open source software library for automated machine learning [AutoML]
  • Lucid - A collection of infrastructure and tools for research in neural network interpretability
  • HiddenLayer - Neural network graphs and training metrics for PyTorch and Tensorflow
  • TensorSpace - Neural network 3D visualization framework
  • Weights & Biases - Experiment Tracking for Deep Learning
  • khaiii - Kakao Hangul Analyzer III
  • Xfer - Transfer Learning framework written in Python
  • CLaF: Clova Language Framework
  • Pytorch Lightning
• Dataset
  • Fueling the Gold Rush: The Greatest Public Datasets for AI
  • Awesome Public Datasets
  • Fashion-MNIST
  • Google Dataset Search
  • 가장 큰 오픈소스 자율주행차량 데이터셋 공개 - UC Berkeley BDD100K
  • Handwritten Hangul Datasets: PE92, SERI95, and HanDB
  • Tencent ML-Images
  • KorQuAD - The Korean Question Answering Dataset
  • VisualData - Discover Computer Vision Datasets
  • 한국어 NLP dataset 모음
  • Korpora: Korean Corpora Archives

Reinforcement Learning

• Fundamental of Reinforcement Learning
• OpenAI : A non-profit artificial intelligence research company
• Reinforcement Learning 그리고 OpenAI
• LEARNING REINFORCEMENT LEARNING (WITH CODE, EXERCISES AND SOLUTIONS)
• Deep Q Learning with Keras and Gym
• Minimal Monte Carlo Policy Gradient (REINFORCE) Algorithm Implementation in Keras
• 이슈카님 강화학습 블로그
• Building a deep learning DOOM bot
• A DOOM flavored primer to reinforcement learning
• [ RL ] CS 294: Deep Reinforcement Learning —(1) Introduction and course overview
• Tutorial: Introduction to Reinforcement Learning with Function Approximation
• Introduction to Markov chains
• [리뷰] DEVIEW : 쿠키런 AI 구현하기
• 딥 강화학습 쉽게 이해하기
• Reinforcement Learning
• 모두의 알파고
• Torch DQN 강화학습 소개
• Doom Bots in TensorFlow
• Keras plays catch, a single file Reinforcement Learning example
• Demystifying Deep Reinforcement Learning
• Deep Reinforcement Learning with Neon
• jayyang님의 머신러닝 블로그
• Introduction to Q-Learning
• Practical Reinforcement Learning
• A Deep Learning Research Review of Reinforcement Learning
• Playing Atari with Deep Reinforcement Learning
• Minimal and Clean Reinforcement Learning Examples
• [IGC] 엔씨소프트 이경종 - 강화 학습을 이용한 NPC AI 구현
• Deep Reinforcement Learning
• TensorForce: A TensorFlow library for applied reinforcement learning
• Introduction to reinforcement learning and OpenAI Gym
• Tic-Tac-Toe-Machine-Leaning-Using-Reinforcement-Learning
• Deep Q-Learning with Pytorch
• [한국어] Safe Multi-Agent Reinforcement Learning for Autonomous Driving
• Reinforcement learning for complex goals, using TensorFlow
• Reinforcement Learning w/ Keras + OpenAI: Actor-Critic Models
• Deep Reinforcement Learning, Decision Making, and Control - ICML 2017 Tutorial
• Open-AI의 gym 이용해 강화학습 훈련하기 1: Q-learning
• 실용주의 머신러닝 6회차 [Jeju ML camp 2017] - Deep Reinforcement Learning based Self Driving Car : [Code]
• Introduction to Imitation Learning
• 파이썬과 케라스로 배우는 강화학습 저자특강
• 알아두면 쓸데있는 신기한 강화학습 NAVER 2017 : [Video]
• 스타2 강화학습 튜토리얼
  • 1편 : [English]
  • 2편 : [English]
  • 3편
• Contextual Bandits and Reinforcement Learning
• RLCode와 A3C 쉽고 깊게 이해하기 : [Video]
• Introduction of Deep Reinforcement Learning : [Video]
• 5 Ways to Get Started with Reinforcement Learning
• 강화학습 공부 로드맵
• 게임과 AI #1 심층강화학습과 AI
• 게임과 AI #2 블레이드 & 소울과 게임 AI Part. 1
• Reinforcement learning on stock trading
• Deep RL Bootcamp
• 스타크래프트2 강화학습
• 슈퍼마리오에 모두를 위한 RL 수업의 딥러닝 코드 붙이기
• 알파고는 스스로 신의 경지에 올랐다
• CNTK 2.1 + Keras + Reinforcement Learning in Python with Flapping Bird
• AlphaGo Zero Explained In One Diagram
• [카카오AI리포트]강화학습 & 슈퍼마리오 part1
• 강화학습으로 풀어보는 슈퍼마리오 part 2.
• Teaching an Actor-Critic Agent Through Optimal Scripted Agent Trajectories
• Doing Deep Reinforcement learning with PPO
• Direct Future Prediction - Supervised Learning for Reinforcement Learning
• Introduction to Various Reinforcement Learning Algorithms
  • Part I [Q-Learning, SARSA, DQN, DDPG]
  • Part II [TRPO, PPO]
• Reinforcement Learning - 첫번째 이야기
• 강화학습으로 똑똑한 슈퍼마리오 만들기
• How to build your own AlphaZero AI using Python and Keras
• 강화학습 소개 - 이동민님
  • 강화학습의 개요
  • 강화학습 알고리즘의 흐름도 Part 1
  • 강화학습 알고리즘의 흐름도 Part 2
• Monte Carlo Tree Search – beginners guide
• My Journey to Reinforcement Learning — Part 0: Introduction
• My Journey to Reinforcement Learning — Part 1: Q-Learning with Table
• My Journey to Reinforcement Learning — Part 1.5: Simple Binary Image Transformation with Q-Learning
• Multi-armed Bandits
• An introduction to Reinforcement Learning
• reinforcement_learning_an_introduction
• Hallucinogenic Deep Reinforcement Learning Using Python and Keras
• How I built an AI to play Dino Run
• Build an AI to play Dino Run
• RL Basics: 1. Markov Process
• RL: 2. Markov Decision Process
• 강화학습에 대한 기본적인 알고리즘 구현
• 안.전.제.일. 강화학습!
• 강화학습 기초부터 DQN까지 [Reinforcement Learning from Basics to DQN]
• Rl from scratch part1
• Rl from scratch part2
• Rl from scratch part3
• Rl from scratch part4
• Rl from scratch part5
• Rl from scratch part6
• Rl from scratch part7
• From REINFORCE to PPO
• AI in Video Games: Improving Decision Making in League of Legends using Markov Chains, Real Match Statistics and Personal Preferences
• Python Implementation of Reinforcement Learning: An Introduction
• Deep Reinforcement Learning Course
• Safe Reinforcement Learning
• 웅이님의 강화학습 블로그
• 인공지능 슈퍼마리오의 거의 모든 것[Pycon 2018 정원석]
• The Future with Reinforcement Learning — Part 1
• 모두를 위한 PG여행 가이드
• DQN Break
• Tutorial: Double Deep Q-Learning with Dueling Network Architecture
• 강화학습의 이론과 실제
• 한국인공지능연구소 1기 강화학습랩 결과보고서
• 강화학습으로 인공지능 슈퍼마리오 만들기 강의 1편
• Reinforcement Learning: a comprehensive introduction [Part 0]
• Advantage Actor Critic Review
• From Scratch: AI Balancing Act in 50 Lines of Python
• Introduction: Reinforcement Learning with OpenAI Gym
• Google Dopamine: New Reinforcement Learning framework
• CS 294-112 at UC Berkeley - Deep Reinforcement Learning
• An intro to Advantage Actor Critic methods: let’s play Sonic the Hedgehog!
• Paper Reading: [Learning to Drive in a day]
• 강화학습[Reinforcement Learning]으로 접근하는 E-commerce Dynamic Pricing 논문리뷰
• Introduction to Reinforcement Learning
• Open sourcing TRFL: a library of reinforcement learning building blocks
• Simple Beginner’s guide to Reinforcement Learning & its implementation
• RL - Introduction to Deep Reinforcement Learning
• On "solving" Montezuma’s Revenge
• 팡요랩 Pang-Yo Lab
• About Deep Reinforcement Learning based on CS294
• 강화학습을 이용한 슈퍼마리오 만들기 튜토리얼
• 퐁 DQN
• 슈퍼마리오 DQN
• Horizon: The first open source reinforcement learning platform for large-scale products and services
• Schooling Flappy Bird: A Reinforcement Learning Tutorial
• Deep [Double] Q-Learning
• Mastering Deep Reinforcement Learning with OpenAI’s new ‘Spinning Up in Deep RL’ package
• Spinning Up in Deep RL
• How to teach AI to play Games: Deep Reinforcement Learning
• How to study Reinforcement Learning
• Explained: Curiosity-Driven Learning in RL— Exploration By Random Network Distillation
• Reinforcement Learning with Python
• Facebook’s Open-Source Reinforcement Learning Platform — A Deep Dive
• 12월 19일. 대전에서 강화학습 강의를 위한 자료
• 강화학습 이론 및 실습
• Reinforcement Learning from Scratch: Designing and Solving a Task All Within a Python Notebook
• Playing Super Mario Bros with Proximal Policy Optimization
• Qrash Course: Reinforcement Learning 101 & Deep Q Networks in 10 Minutes
• Playing Pong using Reinforcement Learning
• A Beginner’s Guide To Reinforcement Learning With A Mario Bros Example
• What follows AlphaStar for Academic AI Researchers?
• AlphaStar: Mastering the Real-Time Strategy Game StarCraft II : [번역]
• Talk: An Introduction to Reinforcement Learning
• AI가 스타크래프트2를 정복한 원리[알파스타]
• [쉽게구현하는 강화학습 1화] Policy Gradient - REINFORCE와 Actor-Critic 구현하기!
• [쉽게구현하는 강화학습 2화] DQN 알고리즘 구현!
• On Choosing a Deep Reinforcement Learning Library
• Learning to play snake at 1 million FPS
• Implementations of basic RL algorithms with minimal lines of codes! (pytorch based)
• Explainable Deep Reinforcement Learning
• 강화학습 해부학 교실: Rainbow 이론부터 구현까지 (2nd dlcat in Daejeon)
• From Zero to Flagpole Hero
• 모두를 위한 강화학습
• Repository for slides & codes of RL Korea Bootcamp
• Reinforcement Learning Concept on Cart-Pole with DQN
• Reinforcement Learning
• RaptorJung님의 강화학습 블로그
• Colab for the RL tutorial at EEML 2020
• 딥레이서 도전하기 - AWSKRUG Deepracer Meetup
• Solutions of team "liveinparis" with codes (6th place) : [Code]

Machine Learning

• Machine Learning Top 10 Articles for the Past Year (v.2017)
• Natural Language Processing using Word2Vec
• 6 Fun Machine Learning Projects for Beginners
• 50+ Data Science, Machine Learning Cheat Sheets, updated
• Prophet: forecasting at scale - Time Series Data Analysis
• Paper Reading : Enriching word vectors with subword information(2016)
• 머신러닝, 제대로 배우는 법
• Predicting Breast Cancer Using Apache Spark Machine Learning Logistic Regression
• 2016 여름 머신러닝 워크샵 1일차 강의 (KAIST 오혜연 교수님)
• 휴먼 러닝 : 머신 러닝 학습 노트
• Word2Vec Vector Algebra Comparison - Python(Gensim) VS Scala(Spark)
• The Amazing Power of Word Vectors
• Word2Vec, Bag-Of-Words
• word2vec 관련 이론 정리
• Machine Learning Recipes with Josh Gordon
• How to use pre-trained word vectors from Facebook’s fastText
• 한국어와 NLTK, Gensim의 만남
• Winning Tips on Machine Learning Competitions by Kazanova, Current Kaggle #3
• Applying Machine Learning To March Madness
• Scikit-Learn Tutorial Series
• 머신 러닝 뉴스 주제 분류
• Transfer Learning - Machine Learning's Next Frontier
• [용어 정리] 입개발자를 위한 Accuracy, Precision, Recall
• Ultimate Guide to Understand & Implement Natural Language Processing
• [용어 정리] 입 개발자를 위한 TF-IDF
• PRML[Pattern Recognition & Machien Learning, Bishop] 을 정리한 문서
• 머신러닝 기반 주차 문제 예측 시스템 개발기 by Google
• Machine learning methods - infographic
• Modern Machine Learning Algorithms: Strengths and Weaknesses
• Dimensionality Reduction Algorithms: Strengths and Weaknesses
• 머신러닝 모델링 알고리즘
• A Collection of Jupyter Notebooks for Machine Learning
• Tuning Your DBMS Automatically with Machine Learning
• End to End Application for Monitoring Real-Time Uber Data Using Apache APIs: Kafka, Spark, HBase – Part 4: Spark Streaming, DataFrames, and HBase
• Coursera Machine Learning으로 기계학습 배우기
• Brief Introduction to Machine Learning without Deep Learning
• SOM: Self Organazing Map 으로 Clustering 코드구현 까지
• Prophet - facebook 의 시계열예측 API
• [선형대수학 #4] 특이값 분해[Singular Value Decomposition, SVD]의 활용
• Facebook Prophet
• Machine Learning 강의노트
• Churn Prediction with Apache Spark Machine Learning
• MNIST 시각화 - 차원 감소
• precision, recall의 이해
• SVD와 PCA, 그리고 잠재의미분석[LSA]
• ElasticSearch Machine Learning
  • 머신러닝으로 시스템의 이상 징후를 자동으로 파악하기
  • 머신러닝으로 시스템 이상 징후를 자동으로 분석하는 예제 따라하기
  • 머신 러닝으로 서버 만 대의 시스템 이상 징후를 자동으로 식별하기
  • 머신러닝으로 1,000개의 프로세스 중에 다르게 행동하는 것들을 프로파일링해서 자동으로 식별하는 예제 따라하기
  • 엘라스틱서치와 머신러닝으로 SIEM의 CEF 데이터를 분석하여 이상 행동하는 프로세스를 자동으로 식별하는 예제
• Gaussian Process Regression tutorial
• 머신러닝을 위한 기초 수학 살펴보기 by mingrammer
• Kaggle 뉴욕시 임대 아파트 문제 머신러닝 튜토리얼 - Pycon Korea 2017
• [SPSS 22] ROC 곡선
• Machine Learning Mindmap / Cheatsheet
• Ensemble Learning to Improve Machine Learning Results
• MEET MICHELANGELO: UBER’S MACHINE LEARNING PLATFORM
• Dimensionality Reduction Using t-SNE
• In Raw Numpy: t-SNE
• Interpreting Decision Trees and Random Forests
• 쉽게 씌어진 word2vec
• A Gentle Introduction on Market Basket Analysis — Association Rules
• Kaggle Zero To All
• Visualising high-dimensional datasets using PCA and t-SNE in Python
• Singular Value Decomposition [SVD] Tutorial: Applications, Examples, Exercises
• 빛나유님의 Data Mining 블로그
• Get Started In Machine Learning in 5 Steps
• soynlp - 김형준님의 한국어 분석을 위한 python library
• How to make your data and models interpretable by learning from cognitive science
• [번역]AI 머신러닝을 시작하는 방법에 대한 조언
• Three Effective Feature Selection Strategies
• [AI] The fastest way to identify keywords in news articles — TFIDF with Wikipedia [Python version]
• Linear Regression in Python; Predict The Bay Area’s Home Prices
• Best Method to Learn Essential Machine Learning Skills Fast
• 캐글[Kaggle] 데이터분석 배우기
• 2017년 가을 Azure Machine Learning 스터디 계획 및 자료 관리
• Predict Employee Turnover With Python
• Kaggle-Knowhow[Korean Ver] 한국분들을 위한 Kaggle 자료 모음
• 자연어 처리[NLP] 관련 블로그
• Solving A Simple Classification Problem with Python — Fruits Lovers’ Edition
• Introduction to Kaggle Kernels
• 머신러닝 강의 - 허민석님 : [English]
• 오늘의 캐글[Kaggle] : [Code]
• Interactive Machine Learning: Make Python ‘Lively’ Again
• Machine Learning for Diabetes
• A Kaggle Master Explains Gradient Boosting
• A Gentle Introduction to the Gradient Boosting Algorithm for Machine Learning
• Gradient Boosting 알고리즘: 개념
• Introduction to Boosted Trees [한국어 버전]
• Gradient Boosting from scratch
• XGBoost - eXtreme Gradient Boosting
• A Gentle Introduction to XGBoost for Applied Machine Learning : [번역]
• XGBoost 사용하기
• Kaggle Tutorial - DataCamp
  • EDA & Machine Learning
  • Your First Machine Learning Model
  • Machine Learning with Kaggle: Feature Engineering
  • Kaggle Python Tutorial on Machine Learning
• Ensemble Learning in Machine Learning | Getting Started
• 차원축소 훑어보기 [PCA, SVD, NMF]
• Kaggle Titanic Competition - A Data Science Framework: To Achieve 99% Accuracy
• How to score 0.8134 in Titanic Kaggle Challenge
• General Tips for participating Kaggle Competitions
• 멀티 암드 밴딧[Multi-Armed Bandits]
• 톰슨 샘플링 for Bandits
• 정보 이론: Information Theory 1편
• 정보 이론 2편: KL-Divergence
• Who will survive the shipwreck?! - Kaggle Titanic
• Stacked Regressions : Top 4% on LeaderBoard - Kaggle House Prices
• Using Yelp Data to Predict Restaurant Closure
• Random Forest in Python
• Improving the Random Forest in Python Part 1
• Hyperparameter Tuning the Random Forest in Python
• Time Series Analysis in Python: An Introduction
• Machine Learning Zero-to-Hero: Everything you need in order to compete on Kaggle for the first time, step-by-step!
• Time Series Analysis Tutorial with Python
• End-to-end Distributed ML using AWS EMR, Apache Spark [Pyspark] and MongoDB Tutorial with MillionSongs Data
• How to Handle Imbalanced Classes in Machine Learning
• Introduction to Python Ensembles
• Data ScienceTutorial for Beginners
• Machine Learning Tutorial for Beginners
• Feature Selection and Data Visualization
• 초짜 대학원생의 입장에서 이해하는 Support Vector Machine [1]
• 열 개의 팔을 가진 강도 - Multi Armed Bandit
• Word2vec을 응용한 컨텐츠 클러스터링
• Why, How and When to apply Feature Selection
• Regression 모델 평가 방법
• Minimizing the Negative Log-Likelihood, in Korean [1]
• Minimizing the Negative Log-Likelihood, in Korean [2]
• Dealing with Imbalanced Classes in Machine Learning
• Topic Modeling with Scikit Learn
• An illustrated introduction to the t-SNE algorithm : [Code]
• Gradient Descent[경사하강법]
• Multi-Class Text Classification with Scikit-Learn
• Multi-Class Text Classification with PySpark
• Multi Label Text Classification with Scikit-Learn
• Common Design for Distributed Machine Learning
• Machine Learning Workflow on Diabetes Data : Part 01
• Machine Learning Workflow on Diabetes Data : Part 02
• Kaggle House Prices Advanced Regression Techniques: One hour analysis
• Always start with a stupid model, no exceptions
• How to solve 90% of NLP problems: a step-by-step guide
• Multi-Class Text Classification with Scikit-Learn
• Logistic Regression — Detailed Overview
• Time Series for scikit-learn People Part I: Where's the X Matrix?
• Time Series for scikit-learn People Part II: Autoregressive Forecasting Pipelines
• Topic Modeling with Gensim[Python]
• Topic Modelling in Python with NLTK and Gensim
• Save Lives With 10 Lines of Code: Detecting Parkinson’s with XGBoost
• Machine Learning Study[Boosting 기법 이해]
• Introduction to Bayesian Linear Regression
• A note about finding anomalies
• Machine Learning for Text Classification Using SpaCy in Python
• Interpretable Machine Learning with XGBoost
• Bayesian Linear Regression in Python: Using Machine Learning to Predict Student Grades Part 1
• Bayesian Linear Regression in Python: Using Machine Learning to Predict Student Grades Part 2
• Visualizing data using t-SNE
• Automatic feature extraction with t-SNE
• How to Use Machine Learning to Predict the Quality of Wines : [Code]
• 구글 ML 엔진 - scikit-learn, XGBoost 지원
• PCA using Python [scikit-learn]
• Use the built-in Amazon SageMaker Random Cut Forest algorithm for anomaly detection
• Using Word2Vec for Better Embeddings of Categorical Features
• A visual introduction to machine learning Part I
• A visual introduction to machine learning Part II - Model Tuning and the Bias-Variance Tradeoff
• Facebook’s Field Guide to Machine Learning video series
• Dimensionality Reduction in Machine Learning by stacking PCA and t-SNE
• Python Machine Learning: Scikit-Learn Tutorial
• Running KMeans clustering on Spark
• Using K-Means to analyse hacking attacks
• K-Means Clustering in Python
• Introduction to K-means Clustering
• Clustering with Sklearn
• Python K-Means Data Clustering and finding of the best K
• ELI5: ROC Curve, AUC metrics
• Generate Quick and Accurate Time Series Forecasts using Facebook’s Prophet [with Python & R codes]
• Let’s learn about AUC ROC Curve!
• Bias-Variance Tradeoff
• Another Twitter sentiment analysis with Python — Part 1
• Another Twitter sentiment analysis with Python-Part 2
• Another Twitter sentiment analysis with Python — Part 3 [Zipf’s Law, data visualisation]
• Another Twitter sentiment analysis with Python — Part 4 [Count vectorizer, confusion matrix]
• Another Twitter sentiment analysis with Python — Part 5 [Tfidf vectorizer, model comparison, lexical approach]
• Another Twitter sentiment analysis with Python — Part 6 [Doc2Vec]
• Another Twitter sentiment analysis with Python — Part 7 [Phrase modeling + Doc2Vec]
• Another Twitter sentiment analysis with Python — Part 8 [Dimensionality reduction: Chi2, PCA]
• Another Twitter sentiment analysis with Python — Part 9 [Neural Networks with Tfidf vectors using Keras]
• Another Twitter sentiment analysis with Python — Part 10 [Neural Network with Doc2Vec/Word2Vec/GloVe]
• Another Twitter sentiment analysis with Python — Part 11 [CNN + Word2Vec]
• Sentiment Analysis with PySpark
• Yet Another Twitter Sentiment Analysis Part 1 — tackling class imbalance
• In Depth: Parameter tuning for Gradient Boosting
• Understanding Random Forests Classifiers in Python
• Unsupervised Learning with Python
• Predicting the Survival of Titanic Passengers
• A Complete Machine Learning Project Walk-Through in Python: Part One
• A Complete Machine Learning Walk-Through in Python: Part Two
• A Complete Machine Learning Walk-Through in Python: Part Three
• Automated Machine Learning on the Cloud in Python
• Machine Learning Kaggle Competition Part One: Getting Started
• Machine Learning Kaggle Competition Part Two: Improving
• Automated Feature Engineering in Python
• A Feature Selection Tool for Machine Learning in Python
• A Conceptual Explanation of Bayesian Model-Based Hyperparameter Optimization for Machine Learning
• An Introductory Example of Bayesian Optimization in Python with Hyperopt
• Automated Machine Learning Hyperparameter Tuning in Python
• Machine Learning Kaggle Competition: Part Three Optimization
• Why Automated Feature Engineering Will Change the Way You Do Machine Learning
• Time Series - Machine Learning Mastery
• Comprehensive Guide to Time Series Analytics, Visualization and Prediction with Python
• 푸른생선의 신바람 금융바다 - 통계, Time Series 데이터 분석
• ARIMA, Python으로 하는 시계열분석 [feat. 비트코인 가격예측]
• A comprehensive beginner’s guide to create a Time Series Forecast [with Codes in Python]
• Time Series Visualization and Forecasting
• Koshort - 한국어 NLP를 위한 high-level API 프로젝트
• The Logistic Regression Algorithm
• Apache Spark and Amazon SageMaker, the Infinity Gems of analytics
• Using Chalice to serve SageMaker predictions
• Trend, Seasonality, Moving Average, Auto Regressive Model : My Journey to Time Series Data with Interactive Code
• Building Trust in Machine Learning Models [using LIME in Python]
• Using categorical data in machine learning with python
• Time Series Analysis for Financial Data I— Stationarity, Autocorrelation and White Noise
• Time Series Analysis for Financial Data II — Auto-Regressive Models
• Time Series Analysis for Financial Data III— Moving Average Models
• Time Series Analysis for Financial Data IV— ARMA Models
• Time Series Analysis for Financial Data V — ARIMA Models
• Time Series Analysis for Financial Data VI— GARCH model and predicting SPX returns
• Featuretools - An open source framework for automated feature engineering
• Manage your Machine Learning Lifecycle with MLflow — Part 1.
• Kaggle Fundamentals: The Titanic Competition
• Getting Started with Kaggle: House Prices Competition
• Machine Learning Fundamentals: Predicting Airbnb Prices
• Machine Learning with Python: A Tutorial
• Human Interpretable Machine Learning [Part 1] — The Need and Importance of Model Interpretation
• Kaggle 강의 자료
• Predicting the Status of H-1B Visa Applications
• Sentiment analysis on reviews: Train Test Split, Bootstrapping, Cross Validation & Word Clouds
• K-Means Clustering
• Realtime prediction using Spark Structured Streaming, XGBoost and Scala
• Unboxing Outliers In Machine Learning
• Detecting Financial Fraud Using Machine Learning: Winning the War Against Imbalanced Data
• Using regression models to predict per capita and median household income in NYC
• Philadelphia Housing Data Part-I: Data Analysis
• Philadelphia Housing Data Part-II: Features Engineering
• Philadelphia Housing Data Part-III: Machine Learning
• Approaching a competition on Kaggle: Avito Demand Prediction Challenge [Part 1] : [Code]
• Approaching a competition on Kaggle: Avito Demand Prediction Challenge [Part 2]
• Approaching a competition on Kaggle: Avito Demand Prediction Challenge [Part 3 — linear regression]
• Chapter-1 Machine Learning Introduction
• Chapter-2 Data and It’s Different Types
• Chapter-3 Bias and Variance Trade-off in Machine Learning
• Learn How to Code and Deploy Machine Learning Models on Structured Streaming
• 광고 클릭 예측을 통해 페이스북이 얻은 실용적인 교훈
• K-Means Clustering in Python with scikit-learn
• Effective Outlier Detection Techniques in Machine Learning
• Topic Modeling and Latent Dirichlet Allocation [LDA] in Python
• 데이터로부터 정보 추출해내기 [Feature Engineering]
• 불균형 데이터셋의 처리를 위한 training data의 처리
• Introduction to Clinical Natural Language Processing: Predicting Hospital Readmission with Discharge Summaries
• Using XGBoost in Python
• Support Vector Machines with Scikit-learn
• Understanding Model Predictions with LIME
• Machine Learning Rules in a Nutshell
• Winning solutions of kaggle competitions
• 'Machine Learning Yearning' 책의 한국어 번역
• Machine Learning Yearning 번역문서 목록
• Elbow Clustering for Artificial Intelligence
• Kaggle 튜토리얼
• Serve Machine Learning Models with Clipper
• Optimal Coupon Targeting for Grocery Items: an Instacart Case Study
• A Gentle Introduction to Data Science for Credit Risk Modeling — Part 1
• A Gentle Introduction to Credit Risk Modeling with Data Science — Part 2
• Detecting True and Deceptive Hotel Reviews using Machine Learning
• An End-to-End Project on Time Series Analysis and Forecasting with Python
• Google - ML Universal Guides
• Kaggle Solutions
• Probability for Machine Learning
• 파이썬으로 머신러닝 배우기
• Brewing up custom ML models on AWS SageMaker
• Comparing Multi-Armed Bandit Algorithms on Marketing Use Cases
• DBSCAN: A Macroscopic Investigation in Python
• Improve Your Model Performance using Cross Validation [in Python and R]
• Gradient Descent — Demystified
• Introduction to Automated Feature Engineering Using Deep Feature Synthesis [DFS]
• Application of Machine Learning Techniques to Trading
• Predicting Employee Churn in Python
• Hyperparameter Optimization in Machine Learning Models
• Unsupervised Text Summarization using Sentence Embeddings
• Parallelizing Feature Engineering with Dask
• Introducing mlflow-apps: A Repository of Sample Applications for MLflow
• Build a model to predict the impact of weather on urban air quality using Amazon SageMaker
• Interactive Machine Learning, Deep Learning and Statistics websites
• Resources for CS 229[Stanford] - Machine Learning
• A "Data Science for Good" Machine Learning Project Walk-Through in Python: Part One
• A "Data Science for Good" Machine Learning Project Walk-Through in Python: Part Two
• How to Visualize a Decision Tree from a Random Forest in Python using Scikit-Learn
• From Big Data to micro-services: how to serve Spark-trained models through AWS lambdas
• The Beginner's Guide to Dimensionality Reduction
• Use Kaggle to start [and guide] your ML/ Data Science journey — Why and How
• A Hands-On Guide to Automated Feature Engineering using Featuretools in Python
• Public repository made for Automated Feature Engineering workshop
• Complete Your First Kaggle Competition [a.k.a Titanic: Machine Learning from Disaster] In Less Than 20 Lines of Code
• Improving Our Code to Obtain a Better Model for Kaggle’s Titanic Competition
• Using Natural Language Processing [NLP], Deep Learning, and GridSearchCV in Kaggle’s Titanic Competition
• The Ultimate Guide to 12 Dimensionality Reduction Techniques [with Python codes]
• Linear Regression Simplified - Ordinary Least Square vs Gradient Descent
• Decision Trees — A Bird's eye view and an Implementation
• Over 200 of the Best Machine Learning, NLP, and Python Tutorials — 2018 Edition
• How to Rank 10% in Your First Kaggle Competition
• Towards Preventing Overfitting: Regularization
• A curated list of awesome anomaly detection resources
• A Kaggler's Guide to Model Stacking in Practice : [번역]
• A Comprehensive Guide to Ensemble Learning [with Python codes]
• Stacking — A Super Learning Technique
• An Implementation and Explanation of the Random Forest in Python
• Interactive Visualization of Decision Trees with Jupyter Widgets
• What’s WRONG with Metrics?
• Pycon korea 2018 kaggle tutorial[kaggle break]
• Ensemble Learning in Python
• Fine tuning a classifier in scikit-learn
• Understanding Logistic Regression in Python
• How to Make Your Machine Learning Models Robust to Outliers
• Trash or treasure — how to tell if a classification algorithm is any good
• ML-Ensemble: Scikit-learn style ensemble learning
• Feature Selection For Machine Learning in Python
• Another Machine Learning Walk-Through and a Challenge
• Differences between L1 and L2 as Loss Function and Regularization
• Smarter Ways to Encode Categorical Data for Machine Learning [Part 1 of 3]
• Decision Tree: an algorithm that works like the human brain
• Feature selection — Correlation and P-value
• The What-If Tool: Code-Free Probing of Machine Learning Models
• Introduction to t-SNE
• Project #2: Predicting House Prices on Kaggle
• Complete Guide to Parameter Tuning in XGBoost [with codes in Python]
• Using AWS SageMaker to Tune Hyperparameter of XG-Boost
• Kaggle Winning Solution Xgboost algorithm -- Let us learn from its author
• XGBoost Hyperparameter Optimization
• Fine-tuning XGBoost in Python like a boss
• How to Win a Data Science Competition: Learn from Top Kagglers
• Linear Regression using Gradient Descent
• Introducing Flint: A time-series library for Apache Spark
• PySpark ML and XGBoost full integration tested on the Kaggle Titanic dataset
• KAGGLE ENSEMBLING GUIDE : [번역]
• Ensemble Modeling : Stack Model Example by J.Thompson [with R]
• Stacking을 위한 패키지 vecstack
• 데이터 분석 패턴, 모형 쌓기[Model Stacking]
• Mlxtend [machine learning extensions] is a Python library of useful tools for the day-to-day data science tasks
• TPOT in Python
• Google Colab과 Kaggle 연동하기
• Amazon SageMaker Workshop
• Beginner's Guide to Feature Selection in Python
• A machine learning survival kit for doctors
• Which encoding is good for time-validation?-1.4417
• Boosting, Bagging, and Stacking — Ensemble Methods with sklearn and mlens
• Python Implementation of Andrew Ng’s Machine Learning Course [Part 1]
• Python Implementation of Andrew Ng’s Machine Learning Course [Part 2.1]
• Python Implementation of Andrew Ng’s Machine Learning Course [Part 2.2]
• A Guide to using Logistic Regression for Digit Recognition [with Python codes]
• Solving multiarmed bandits: A comparison of epsilon-greedy and Thompson sampling
• 클러스터링을 평가하는 척도 - Mutual Information
• 클러스터링을 평가하는 척도 - Rand Index
• Diving Deep with Imbalanced Data
• Machine Learning for Insights - Kaggle
• Titanic Starter with XGBoost, 173/209 LB
• My First Kaggle Competition - Using Random Forests to predict Housing Prices
• How to deliver on Machine Learning projects
• Analyzing time series data in Pandas
• Large-scale Graph Mining with Spark: Part 1
• Large-scale Graph Mining with Spark: Part 2
• Kaggle Study - 커널 커리큘럼
• Machine Learning Black Friday Dataset
• Why you should try Mean Encoding
• Detect Anomalies in Your Data with Amazon SageMaker
• [FDS] Fraud Detection System with AutoEncoder
• Automated Hyper-parameter Optimization in SageMaker
• Featuretools on Spark
• Mastering The New Generation of Gradient Boosting - CatBoost
• 아파트 시세, 어쩌다 머신러닝까지
• CatBoost vs. Light GBM vs. XGBoost
• Amazon SageMaker를 이용한 예측 분석-남궁영환 솔루션즈 아키텍트, AWS : [Video]
• Predicting Hospital Readmission for Patients with Diabetes Using Scikit-Learn
• 머신러닝[Machine Learning]과 확률[Probability]
• Scikit-Learn: A silver bullet for basic machine learning
• CHOPT : Automated Hyperparameter Optimization Framework for Cloud-Based Machine Learning Platforms
• Uber Introduces PyML: Their Secret Weapon for Rapid Machine Learning Development
• Ensemble Learning: When everybody takes a guess…I guess!
• The Data Science of K-Pop: Understanding BTS through data and A.I.
• Automatic Feature Engineering: An Event-Driven Approach
• Fundamentals of Anomaly Detection
• Explainable Artificial Intelligence [Part 1] — The Importance of Human Interpretable Machine Learning
• Explainable Artificial Intelligence [Part 2] — Model Interpretation Strategies
• Finding Similar Quora Questions with Word2Vec and Xgboost
• Automating interpretable feature engineering for predicting CLV
• Decrypting your Machine Learning model using LIME
• My secret sauce to be in top 2% of a kaggle competition
• Summary for Practical Tips from fast.ai Machine Learning Course — Part 1
• Summary for Practical Tips from fast.ai Machine Learning Course — Part 2
• Summary for Practical Tips from fast.ai Machine Learning Course — Part 3
• Cutting the Cord: Predicting Customer Churn for a Telecom Company
• How to Create Value with Machine Learning
• Prediction Engineering: How to Set Up Your Machine Learning Problem
• Feature Engineering: What Powers Machine Learning
• Modeling: Teaching a Machine Learning Algorithm to Deliver Business Value
• Coloring With Random Forests
• Intuitive Interpretation of Random Forest
• Using 3D visualizations to tune hyperparameters in ML models
• Automated Feature Engineering for Predictive Modeling
• Machine Learning Ensemble Boosting
• Good Feature Building Techniques — Tricks for Kaggle — My Kaggle Code Repository
• Google News and Leo Tolstoy: Visualizing Word2Vec Word Embeddings using t-SNE
• Simplifying Sentiment Analysis in Python
• Building Machine Learning Engineering Tools
• AdaBoost Classifier in Python
• Why Use K-Means for Time Series Data? [Part One]
• Why Use K-Means for Time Series Data? [Part Two]
• Why Use K-Means for Time Series Data? [Part Three]
• Understanding binary cross-entropy / log loss: a visual explanation
• RISE Camp 2018 06 - Clipper: A Low-Latency Online Prediction Serving System, Simon Mo
• Predict Where a New User Will Book Their First Travel Experience
• Solving Multi-Label Classification problems [Case studies included]
• Machine Learning Model for Predicting Click-Through in Hotel Online Ranking
• RFM Analysis Tutorial
• 파이썬을 활용한 자연어 분석 - nltk basic tutorial
• House Prices: Advanced Regression Techniques
• Outlier-Aware Clustering: Beyond K-Means
• Multi lingual text-processing
• With These New Additions, AWS SageMaker is Starting to Look More Real for Data Scientists
• De[Coding] Random Forests
• Tool Review: Lessons learned from using FeatureTools to simplify the process of Feature Engineering
• Simple Automatic Feature Engineering — Using featuretools in Python for Classification
• What to do when your training and testing data come from different distributions
• The 50 Best Public Datasets for Machine Learning
• An Introduction to Random Forest
• Avoiding Parking Tickets in San Francisco Using Data Analytics
• Stacking understanding. Python package for stacking
• XGBoost is not black magic
• Hands-on Machine Learning Model Interpretation
• Using Under-Sampling Techniques for Extremely Imbalanced Data
• Exploratory Data Analysis, Feature Engineering and Modelling using Supermarket Sales Data. Part 1.
• 7 Techniques to Handle Imbalanced Data
• How to handle Imbalanced Classification Problems in machine learning?
• How To handle Imbalance Data : Study in Detail
• Interpreting predictive models with Skater: Unboxing model opacity
• Dealing With Class Imbalanced Datasets For Classification.
• Three techniques to improve machine learning model performance with imbalance datasets
• Mean [likelihood] encodings: a comprehensive study
• 6 Ways for Feature Selection
• Introduction to Feature Selection methods with an example [or how to select the right variables?]
• Feature importance and dependence plot with shap
• Boruta feature elimination
• ML-Powered Product categorization for smart shopping options
• Hidden Technical Debt in Machine Learning Systems 리뷰
• Kaggle Past Solutions
• Machine Learning and Music Classification: A Content-Based Filtering Approach
• Automated Keyword Extraction from Articles using NLP
• Synthetic data generation — a must-have skill for new data scientists
• Amazon SageMaker adds Scikit-Learn support
• Customer Segmentation Report for Arvato Financial Solutions
• Let’s Try t-SNE!
• Using Machine Learning Models for Breast Cancer Detection
• How to avoid the Machine Learning blackbox with SHAP
• Understanding how LIME explains predictions
• 머신러닝 오퍼레이션 자동화, MLOps
• Use Unsupervised Machine Learning To Find Potential Buyers of Your Products
• Introduction to AI 강좌
• End To End Guide For Machine Learning Project
• Hyperparameters tunning with Hyperopt
• Review Rating Prediction: A Combined Approach
• How to Select Your Final Models in a Kaggle Competition
• Distributed Data Pre-processing using Dask, Amazon ECS and Python [Part 1]
• Distributed Data Pre-processing using Dask, Amazon ECS and Python [Part 2]
• Classify Songs Genres From Audio Data
• Improving Data Quality with Product Similarity Search
• PyCM - Multi-class confusion matrix library in Python
• Hyperopt - Documentation for saving and reloading evaluations with Trials
• Implementing a Profitable Promotional Strategy for Starbucks with Machine Learning [Part 1]
• Implementing a Profitable Promotional Strategy for Starbucks with Machine Learning [Part 2]
• Kaggle - Smote the training sets
• DEALING WITH IMBALANCED DATA: UNDERSAMPLING, OVERSAMPLING AND PROPER CROSS-VALIDATION
• The Right Way to Oversample in Predictive Modeling
• Kaggle - 2nd Place Lightgbm Solution
• Kaggle - LIghtGBM CV [LB .282]
• 6 Different Ways to Compensate for Missing Data [Data Imputation with examples]
• promotionImpact - 프로모션 효과 분석용 R 패키지
• Kaggle - Basic end-to-end training of a LightGBM model
• [카카오AI리포트]머신러닝 적용의 실제
• Kaggle - Updated Bayesian + LGBM_XGB_CAT+ FE + Kfold + CV
• Kaggle - Stacking Test-Sklearn, XGBoost, CatBoost, LightGBM
• Tips and tricks to win kaggle data science competitions
• Automate Stacking In Python
• Feature Engineering
• Winning Kaggle Competitions
• Hands-on: Predict Customer Churn
• Detecting Credit Card Fraud Using Machine Learning
• Improve your workflow by managing your machine learning experiments using Sacred
• Why Feature Correlation Matters …. A Lot!
• What We Learned by Serving Machine Learning Models at Scale Using Amazon SageMaker
• TentionFlow
• Introduction to Machine Learning on Apache Spark
• Anomaly Detection: Part 1
• Anomaly Detection: Part 2
• 조윤주님의 Time Series 시계열 분석 블로그
• Time-step wise feature importance in deep learning using SHAP
• Time Series of Price Anomaly Detection
• AutoML - Automatic Machine Learning Challenge & Lessons
• Handling imbalanced datasets in machine learning
• A Kaggle Master Explains Gradient Boosting
• Introducing Snorkel
• From Pandas to Scikit-Learn — A new exciting workflow
• NLP Kaggle Competition
• Unsupervised learning for anomaly detection in stock options pricing
• Manifold: A Model-Agnostic Visual Debugging Tool for Machine Learning at Uber
• Natural Language Processing Using Stanford’s CoreNLP
• PySpark in Google Colab
• A map for Machine Learning on AWS
• Talk: From Notebook to Production with Amazon SageMaker
• Scaling Machine Learning from 0 to millions of users — part 1
• Scaling Machine Learning from 0 to millions of users — part 2
• Introduction to StanfordNLP: An NLP Library for 53 Languages [with Python code]
• Explaining Feature Importance by example of a Random Forest
• Predicting Wine Quality using Text Reviews
• Building fully custom machine learning models on AWS SageMaker: a practical guide
• Introduction to gradient boosting on decision trees with Catboost
• 7 Amazing Open Source NLP Tools to Try With Notebooks in 2019
• Feature Selection with sklearn and Pandas
• What my first Silver Medal taught me about Text Classification and Kaggle in general?
• Machine Learning Explainability
• Adversarial Validation example for VSB Power Line Fault Detection
• Anomaly Detection Strategies for IoT Sensors
• How to Win a Data Science Competition: Learn from Top Kagglers | Advanced Machine Learning Specialization
• 9 General Kaggle Tips
• Real-Time Streaming and Anomaly detection Pipeline on AWS
• 이유한님의 Kaggle Study
• 실전 이탈 예측 모델링을 위한 세 가지 고려 사항 #1
• 실전 이탈 예측 모델링을 위한 세 가지 고려 사항 #2
• Preprocess input data before making predictions using Amazon SageMaker inference pipelines and Scikit-learn
• Category Encoders
• [번역] 머신러닝을 활용한 제품 카테고리 분류하기
• What Causes Heart Disease? Explaining the Model
• Using word2vec to Analyze News Headlines and Predict Article Success
• What is a Hypothesis in Machine Learning?
• How to train Boosted Trees models in TensorFlow
• 5 Ways to Detect Outliers/Anomalies That Every Data Scientist Should Know [Python Code]
• How to explain any machine learning model prediction
• Why Model Explainability is The Next Data Science Superpower
• My First Kaggle Competition
• EDA, 데이터 설명서에서 시작하기
• Building an Employee Churn Model in Python to Develop a Strategic Retention Plan
• Binary Classifier Evaluation made easy with HandySpark
• Kaggle Days Paris Youtube
• A/B Testing with Machine Learning - A Step-by-Step Tutorial
• Generating Synthetic Classification Data using Scikit
• Cross-Validation for Imbalanced Datasets
• How to not overfit?
• [Kaggle Beginner] 캐글 초보자를 위한 10가지 팁
• Towards DataScience - Project Kaggle
• FastText sentiment analysis for tweets: A straightforward guide.
• Machine Learning-Powered Search Ranking of Airbnb Experiences
• Do you know how to choose the right machine learning algorithm among 7 different types?[KR]
• Augmenting categorical datasets with synthetic data for machine learning.
• Feature Engineering & Importance Testing
• Build XGBoost / LightGBM models on large datasets — what are the possible solutions?
• A “full-stack” data science project
• Model Stacking을 통한 Ensemble 방법
• An interesting and intuitive view of AUC and ROC curve
• Time Series Machine Learning Regression Framework
• Introduction to Anomaly Detection in Python
• Kaggle Credit Scoring data science competition
• Meet Michelangelo - Uber’s Machine Learning Platform [Korean]
• Bias and Variance [편향과 분산]
• Time Series Feature Extraction for industrial big data [IIoT] applications
• Automating interpretable feature engineering for predicting CLV
• Build an end-to-end Machine Learning Model with MLlib in pySpark.
• Pruned Cross Validation for faster hyperparameter optimization
• Ensemble methods: bagging, boosting and stacking
• Simplify machine learning with XGBoost and Amazon SageMaker
• Feature Selection with Null Importances : [번역]
• AWS re:Invent 2018: Integrate Amazon SageMaker with Apache Spark, ft. Moody's [AIM403-R1] : [Video]
• Achieving a top 5% position in an ML competition with AutoML
• Using the ‘What-If Tool’ to investigate Machine Learning models.
• Being a Data Scientist does not make you a Software Engineer!
• Architecting a Machine Learning Pipeline
• Cross-Validation Strategies for Time Series Forecasting
• Portfolio-Scale Machine Learning at Zynga
• Overview of the different approaches to putting Machine Learning (ML) models in production
• Analyzing Tweets with NLP in minutes with Spark, Optimus and Twint
• Using Apache Spark to Predict Installer Retention from Messy Clickstream Data
• Outlier Detection and Treatment: A Beginner's Guide
• How to Generate Prediction Intervals with Scikit-Learn and Python
• Build end-to-end machine learning workflows with Amazon SageMaker and Apache Airflow
• Need for Feature Engineering in Machine Learning
• MLFlow: Platform for Complete Machine Learning Lifecycle
• Detecting Patterns with Unsupervised Learning
• Machine Learning Deployment using AWS SageMaker
• 적당한 ‘정확도’가 보장되는 모델을 ‘자동으로’ 만들 수는 없을까?
• Scikit Learn predictions on Apache Spark
• Machine Learning Algorithm Visualization
• Kaggle - Youtube
• The Hitchhiker’s Guide to Feature Extraction
• ML Approaches for Time Series
• Everything you can do with a time series : [번역]
• Normalization vs Standardization — Quantitative analysis : [번역]
• Building Production Machine Learning Systems
• Fraud Detection: Give me reasons
• Reaching the depths of (power/geometric) ensembling when targeting the AUC metric
• An introduction to model ensembling
• Brute force techniques of variable selection for classification problems
• What I Learned from (Two-time) Kaggle Grandmaster Abhishek Thakur
• Multi-Class Text Classification Using PySpark, MLlib & Doc2Vec
• [이유한님] 캐글 코리아 캐글 스터디 커널 커리큘럼
• Gaussian Mixture Models Explained
• Intro to Feature Selection Methods for Data Science
• Normalization vs Standardization — Quantitative analysis
• 머신러닝 - 수식 없이 이해하는 Gaussian Mixture Model (GMM)
• Kaggle script build system template
• A curated list of applied machine learning and data science notebooks and libraries across different industries.
• Fast auc roc computation
• End-to-End Time Series Interpolation in PySpark — Filling the Gap
• Deep Dive into Catboost Functionalities for Model Interpretation
• Amazon SageMaker Ground Truth: Using A Pre-Trained Model for Faster Data Labeling
• Recall, Precision, F1, ROC, AUC, and everything
• Associating prediction results with input data using Amazon SageMaker Batch Transform
• How To Use Active Learning To Iteratively Improve Your Machine Learning Models
• Softmax Activation Function
• Label Smoothing: Making model robust to incorrect labels
• I’m Kaggler, Why need kaggle?
• Financial data modeling with RAPIDS.
• How to visualize decision trees
• Getting Deeper into Categorical Encodings for Machine Learning
• Opening Black Boxes: How to leverage Explainable Machine Learning
• Log Book — Guide to Distance Measuring Approaches for K- Means Clustering
• Cluster Analysis: Create, Visualize and Interpret Customer Segments
• The 5 Feature Selection Algorithms every Data Scientist should know
• How to Build First CC Fraud Model using CatBoost
• Feature Engineer Optimization in HyperparameterHunter 3.0
• 머신러닝 실험을 도와줄 Python Sacred 소개
• Sacred와 Omniboard를 활용한 실험 및 로그 모니터링
• 4 Tips for Advanced Feature Engineering and Preprocessing
• Getting started with Text Preprocessing
• Interpretable Machine Learning 개요: (1) 머신러닝 모델에 대한 해석력 확보를 위한 방법
• Interpretable Machine Learning 개요: (2) 이미지 인식 문제에서의 딥러닝 모델의 주요 해석 방법
• A story of my first gold medal in one Kaggle competition: things done and lessons learned
• XGBoost 정리
• XGBoost 사용시 GPU 가속하기
• Beginner Guide : Feature Selection
• Train sklearn 100x faster
• Introducing Feast: an open source feature store for machine learning
• Tracking ML Experiments using MLflow
• All About Missing Data Handling
• Beginner Guide : Missing Data Handling
• Introducing SHAP Decision Plots
• How to LB probe on Kaggle
• Part 1. Introduction to Ensemble Learning
• Part 2. Introduction to Ensemble Learning : Boosting
• Explain Your Model with the SHAP Values
• Machine Learning Workspace - All-in-one web-based development environment for machine learning
• Data Science Glossary on Kaggle
• Encode Smarter: How to Easily Integrate Categorical Encoding into Your Machine Learning Pipeline
• Black-Box models are actually more explainable than a Logistic Regression
• Ranking features based on predictive power/importance of the class labels
• Adding Interpretability to Multiclass Text Classification models
• Feature Selection: Beyond feature importance?
• The Ultimate Tool for Data Science Feature Factories
• Feature Factories pt 2: An Introduction to MLFlow
• Version Control for Data Science — Tracking Machine Learning models and datasets
• EmbeddingEncoder + AutoLGB in Kaggler
• Deploying Models to Production with Mlflow and Amazon Sagemaker
• MLOps Tooling
• Turn Python Scripts into Beautiful ML Tools
• 11 Categorical Encoders and Benchmark
• What happens when ML pipeline meets Hydra?
• 머신러닝으로 콘서트 티켓 판매량 예측하기(1) 콘서트 비즈니스에서 예측의 역할 — MyMusicTaste
• 머신러닝으로 콘서트 티켓 판매량 예측하기(2) 모델링, 첫 삽을 뜨기까지— MyMusicTaste
• 머신러닝으로 콘서트 티켓 판매량 예측하기(3) 첫 번째 모델, Paul의 탄생 — MyMusicTaste
• 머신러닝으로 콘서트 티켓 판매량 예측하기(4) 모델 개선 프로세스와 지표— MyMusicTaste
• Time Series Analysis in Python – A Comprehensive Guide with Examples
• 클라우드 환경에서 머신러닝 서비스 프로토타입 빠르게 만들어보기
• LightGBM, XGBoost Hyper Parameteres
• NGBoost Explained
• Interpreting Machine Learning Model
• Statistical Modeling — The Full Pragmatic Guide
• Game Theory to Interpret Machine Learning Models and Predictions — The Ultimate Guide
• Automating ML Feature Engineering
• Explain Any Models with the SHAP Values — Use the KernelExplainer
• Federated Learning: Challenges, Methods, and Future Directions
• 파이썬을 활용한 자연어 분석
• PySpark & AWS | Predicting Customer Churn
• Forecasting in Python with Facebook Prophet
• Google’s new ‘Explainable AI” (xAI) service
• Matthews Correlation Coefficient Is The Best Classification Metric You’ve Never Heard Of
• Text Classification with Extremely Small Datasets
• Using Gradient Boosting for Time Series prediction tasks
• [re:Invent 2019 워크샵] Optimizing your machine learning models on Amazon SageMaker
• Feature Engineering Time
• Using Spark to Predict Churn
• Open-Sourcing Metaflow, a Human-Centric Framework for Data Science
• LightGBM with the Focal Loss for imbalanced datasets
• Hypothesis Testing in Machine Learning: What for and Why
• Stacking made easy with Sklearn
• Rules of Machine Learning: Best Practices for ML Engineering 정리
• Three Model Explanability Methods Every Data Scientist Should Know
• Stratified Group k-Fold Cross-Validation
• The Winding Road to Better Machine Learning Infrastructure Through Tensorflow Extended and Kubeflow
• Scaling Machine Learning Algorithms(Fbprophet/XGBoost) with pyspark on W-MLP
• Introducing Xverse! — A python package for feature selection and transformation
• Explaining black box models-Ensemble and Deep Learning using LIME and SHAP
• Practical Lessons for Scaling Machine Learning Solutions in the Real World
• NLP 튜토리얼: 라벨링 없이 트위터 유저들을 자동으로 나누어보기
• How to build machine learning model at large scale with Apache Spark and LightGBM for credit card fraud detection?
• How to succeed in code (kernel) competitions | Dmitry Gordeev | Kaggle Days
• Machine Learning의 Feature Store란?
• MNIST 2D t-SNE with Rapids
• A new perspective on Shapley values: the Naïve Shapley method
• Building a real-time prediction pipeline using Spark Structured Streaming and Microservices
• Kaggler Pipeline for Data Science Competitions
• Explain Your Model with Microsoft’s InterpretML
• Kubeflow 1.0: Cloud-Native ML for Everyone
• How To Painlessly Analyze Your Time Series
• AWS Korea AI/ML Workshop
• Active Learning: getting the most out of limited data
• 지구별 여행자님의 Kubeflow Blog
• Experiment Management: How to Organize Your Model Development Process
• 15 Best Tools for Tracking Machine Learning Experiments
• 10 Useful ML Practices For Python Developers
• 머신러닝 Experiment Management 쉽게 하기(feat. neptune ai)
• Share ML with KubeFlow and MLflow
• Bring your own model for Amazon SageMaker labeling workflows with active learning
• kubeflow pipeline 사용해보기 - kubeflow pipeline example with iris data
• SHAP formula explained the way I wish someone explained it to me
• Making Sense of Shapley Values
• A True End-to-End ML Example: Lead Scoring
• RIP correlation. Introducing the Predictive Power Score
• Anomaly Detection with PyOD!
• Hydra — A fresh look at configuration for machine learning projects
• MLOps: Continuous delivery and automation pipelines in machine learning 번역
• Introducing BentoML: An open-source framework for high-performance model serving
• Anomaly Detection : Isolation Forest with Statistical Rules
• An Awesome Tutorial to Learn Outlier Detection in Python using PyOD Library
• 핸즈온 머신러닝 2 - 박해선님 YouTube 강의
• Guide to Interpretable Machine Learning
• mlmachine - Clean ML Experiments, Elegant EDA & Pandas Pipelines
• 히든마르코프 모델을 이용한 오픈 이온 채널 수 예측(우리가 Kaggle – University of Liverpool – Ion Switching Competition에서 거의 우승할뻔했던 경험에 대하여)
• 이야기로 설명하는 최대 우도 추정법 (Maximum Likelihood Estimation)
• 이야기로 설명하는 로지스틱 회귀 분석
• Push the limits of explainability — an ultimate guide to SHAP librar
• Machine Learning Visualizations with Yellowbrick
• Feature selection? You are probably doing it wrong
• SHAP에 대한 모든 것 - part 1 : Shapley Values 알아보기
• SHAP에 대한 모든 것 - part 2 : SHAP 소개
• SHAP에 대한 모든 것 - part 3 : SHAP을 통한 시각화해석
• [리뷰] XAI 설명 가능한 인공지능 (인공지능을 해부하다)
• Sagemaker Github Actions
• Use SHAP loss values to debug/monitor your model
• Is this the Best Feature Selection Algorithm “BorutaShap”?
• All Machine Learning Algorithms Explained
• Announcing PyCaret 1.0.0
• 4 Automatic Outlier Detection Algorithms in Python
• Feature Importance May Be Lying To You
• Outlier Detection
• 텍스트 마이닝 기법
• Regression with PyCaret: A better machine learning library
• A Simplified approach using PyCaret for Anomaly Detection
• Machine learning made easier with PyCaret
• An Explanation for eXplainable AI
• Explaining Machine Learning Predictions and Building Trust with LIME
• 6 Dimensionality Reduction Algorithms With Python
• Outlier detection method introduction
• From Hours to Seconds: 100x Faster Boosting, Bagging, and Stacking with RAPIDS cuML and Scikit-learn Machine Learning Model Ensembling
• Shparkley: Scaling Shapley values with Spark for Interpreting Machine Learning Models
• 한국어 자연어처리 1편_서브워드 구축(Subword Tokenizer, huggingface VS SentencePiece)
• Interpreting Anomalies from Isolation Forest
• Introduction to Yellowbrick: A Python Library to Visualize the Prediction of your Machine Learning Model
• Getting oriented in the RAPIDS distributed ML ecosystem, part 1: ETL
• Unsupervised Meta-Learning Is All You Need
• Introduction to Hydra.cc: A Powerful Framework to Configure your Data Science Projects
• Better Features for a Tree-Based Model
• Running on-demand, serverless Apache Spark data processing jobs using Amazon SageMaker managed Spark containers and the Amazon SageMaker SDK
• Introducing MLOps - 차문수(Superb AI) :: 제33회 AWSKRUG DataScience모임
• Machine Learning Visualization
• Optuna vs Hyperopt
• The Unknown Benefits of using a Soft-F1 Loss in Classification Systems
• 5 things you are doing wrong in PyCaret
• 1+1=? Better decision-making when causal inference meets machine learning
• t-SNE로 보는 본 대회 이미지의 특징 (유사도를 중심으로)
• Synthetic Data Vault (SDV): A Python Library for Dataset Modeling
• How to Improve Data Labeling Efficiency with Auto-Labeling, Uncertainty Estimates, and Active Learning
• Machine Learning Case Study: Telco Customer Churn Prediction
• [NLP 언제까지 미룰래? 일단 들어와!!] #1.자연어 처리란?
• [NLP 언제까지 미룰래? 일단 들어와!!] #2. NLP 전처리
• [NLP 언제까지 미룰래? 일단 들어와!!] #3. Vectorization
• [NLP 언제까지 미룰래? 일단 들어와!!] #4. word embedding
• [NLP 언제까지 미룰래? 일단 들어와!!] #5. Modeling(완)
• 머신러닝 시스템 디자인 패턴
• Never Leave the GPU: End-to-end Machine Learning Pipelines with RAPIDS Preprocessing
• Machine Learning Engineering
• [Paper] Please Stop Permuting Features
• Understand your data and ML model using What-IF Tool
• Approaching (Almost) Any Machine Learning Problem
• Causal inference (Part 1 of 3): Understanding the fundamentals
• Causal inference (Part 2 of 3): Selecting algorithms
• Causal inference (Part 3 of 3): Model validation and applications
• Attribution analysis: How to measure impact? (Part 1 of 2)
• Attribution analysis: How to measure impact? (Part 2 of 2)
• Tabular Data Binary Classification: All Tips and Tricks from 5 Kaggle Competitions
• Week 35 - 모델 중심에서 데이터 중심의 AI 개발로
• Tutorial on Causal Inference and its Connections to Machine Learning (Using DoWhy+EconML)
• Getting Started with Causal Inference
• 데이터 과학자가 알아야할 인과적 추론들의 우위관계
• How to Use Causal Inference In Day-to-Day Analytical Work — Part 1 of 2
• How to Use Causal Inference in Day-to-Day Analytical Work — Part 2 of 2
• You are underutilizing shap values — feature groups and correlations
• Causal Inference: What, Why, and How
• Data Science on AWS
• Understanding inverse propensity weighting
• The SHAP with More Elegant Charts
• 자연실험은 어떻게 인과관계 추론에 활용되는가?
• 인과관계를 찾아서 1
• 인과관계를 찾아서 2
• 인과관계를 찾아서 3: 도구변수
• 인과관계를 찾아서 4: 이중차분법
• 도메인 지식이 결여된 인과 추정이 위험한 이유
• 인과추론 분석 설계에서 도메인 지식이 필요한 이유
• 게임 플레이어는 좋은 아이템을 획득하면 게임을 더 열심히 하게 될까?
• 아이템에 대한 만족도는 플레이어가 게임을 열심히 하는데 영향을 미칠까?
• A Quickstart for Causal Analysis Decision-Making with DoWhy
• Estimate the Causal Effect Intervention on Time Series with causalimpact
• Machine Learning Serving - BentoML 사용법
• Kubernetes기반의 Regression Test Pipeline을 구축하기
• Causal Inference — To Control or not to Control
• Causal Inference: 인과 추론 소개
• Quasi-experiments: 인과 관계 (이벤트의 효과) 를 측정하는 방법
• A/B 테스트 결과 해석에서 자주 발생하는 12가지 함정들
• Causal Impact - Getting Started
• Why you should not rely on t-SNE, UMAP or TriMAP
• Supercharge Your Machine Learning Experiments with PyCaret and Gradio
• 매일 사용할지 모르는 간단한 인과추론 방식에 대해서(Confounder)
• [Causality 개념정리] Confounding Factor (Confounder) 란?
• Introduction to Causal Inference
• Foundations of causal inference and its impacts on machine learning webinar
• Google Causal Impact Case Study in Python
• Google Causal Impact in Python with Tensorflow - Tesla buys Bitcoin Case Study
• Python and the Holy Grail of Causal Inference - Dennis Ramondt, Huib Keemink
• Causal Inference With Python Part 1 - Potential Outcomes
• Causal Inference With Python Part 2 - Causal Graphical Models
• Causal Inference With Python Part 3 - Frontdoor Adjustment
• Practical Python Causality: Econometrics for Data Science
• Causal Machine Learning for Econometrics: Causal Forests
• Causal ML for Data Science: Deep Learning with Instrumental Variables
• CausalLift: Python package for Uplift Modeling in real-world business; applicable for both A/B testing and observational data
• CausalLift
• Causal inference for data scientists: a skeptical view
• Causal Networks with Python: Predicting Punching Power in Boxing
• Uplift Modeling: A Quick Introduction
• Beyond Churn: An Introduction to Uplift Modeling
• Mediation Modeling at Uber: Understanding Why Product Changes Work (and Don’t Work)
• Using Causal Inference to Improve the Uber User Experience
• Why start using uplift models for more efficient marketing campaigns
• Causal Inference Book
• Chris Anagnostopoulos: Causal Machine Learning: a rising tide
• CausalNex in Action — Finding the WHY Behind the Scenes
• Causal Data Science
• 4 Reasons why Correlation does NOT imply Causation
• A Crash Course in Causality: Inferring Causal Effects from Observational Data
• Causal Diagrams: Draw Your Assumptions Before Your Conclusions
• Causal design patterns for data analysts
• Causal Design Patterns
• Causal Inference: The Mixtape
• Causal Inference : Primer (2019-06-01 잔디콘)
• Understanding MLOps
• XAI Stories
• Causal Inference — A Brief Introduction
• An Application of Causal Inference
• Causal Inference using Difference in Differences, Causal Impact, and Synthetic Control
• 1. 성향점수 매칭 (Propensity Score matching)
• 아빠가 들려 주는 [통계] Propensity Score Matching and 그외 다른 방법들
• 아빠가 들려 주는 [통계] Propensity Score Matching 엑셀로 해 보자1
• 아빠가 들려 주는 [통계] Propensity Score Matching 엑셀로 해 보자2
• 아빠가 들려 주는 [통계] Inverse Probability of Treatment Weighting IPTW weight의 개념과 실제
• 아빠가 들려 주는 [통계] Inverse Probability of Treatment Weighting IPTW weight의 개념과 실제2
• 아빠가 들려 주는 [통계] Inverse Probability of Treatment Weighting IPTW weight의 개념과 실제3
• Michael Johns: Propensity Score Matching: A Non-experimental Approach to Causal... | PyData NYC 2019
• 단국대 2020년 가을학기 캐글 뽀개기 강좌
• Awesome Causality
• Be Geeky님의 Causal Inference
• Causal Inference Study
• Awesome Causal Inference
• Enterprise Causal Inference: Beyond Churn Modeling
• Improve CPA / ROAS of Digital Advertising Through Causal Inference
• Propensity Scores and Inverse Probability Weighting in Causal Inference
• Be Careful When Interpreting Predictive Models in Search of Causal Insights
• A Quick Comparison of Causal-Inference Estimates
• Jungle Scout case study: Kedro, Airflow, and MLFlow use on production code
• Beyond A/B Testing: Primer on Causal Inference
• A World of Causal Inference with EconML by Microsoft Research
• [MLOps KR 행사] MLOps 춘추 전국 시대 정리(210605) : [Video]
• Ray: 대규모 ML인프라를 위한 분산 시스템 프레임워크(조상빈) : [Video]
• JupyterFlow : 당신의 모델에 날개를 달아드립니다(유홍근) : [Video]
• 모델을 데이터셋에 맞게 대량을 찍어내는 방법(only 파이썬)(김태영) : [Video]
• KRSH: 선언형 Kubeflow, Terraform처럼 파이프라인 관리하기(김완수) : [Video]
• Data-centric MLOps(이정권) : [Video]
• Causal Inference : An Introduction
• Interpretation of Isolation Forest with SHAP
• How to Use Quasi-experiments and Counterfactuals to Build Great Productsa
• From Scratch: Permutation Feature Importance for ML Interpretability
• [XAI] EBM(Explainable Boosting Machine) 알고리즘 소개
• Explaining the predictions— Shapley Values with PySpark
• From Prediction to Action — How to Learn Optimal Policies From Data (Part 1)
• An Ultimate Guide to Matching and Propensity Score Matching
• Leveraging proxy variables for causal inference
• Need for Data-centric ML Platforms
• [XAI] LIME(Local Interpretable Model-agnostic Explanation) 알고리즘
• Causal inference from nonrandomized data key concepts and recent trends
• Towards Causal Representation Learning
• Causal Inference for Data Scientists: Part 1
• 인과추론의 데이터과학 - YouTube
• SHAP: Shapley Additive Explanations
• Understanding LightGBM Parameters (and How to Tune Them)
• Why you should be using PHATE for dimensionality reduction
• 플레이스 AI 개발의 MLOps w/ Kubernetes
• Causal Inference and Machine Learning in Practice with EconML and CausalML
• Time Based Cross Validation
• Transform your categorical columns with imperio SmoothingTransformer
• KL divergence
• Introducing Autofaiss: An Automatic K-Nearest-Neighbor Indexing Library At Scale
• From Prediction to Action — How to Learn Optimal Policies From Data (1/4)
• From Prediction to Action — How to Learn Optimal Policies From Data (2/4)
• From Prediction to Action — How to Learn Optimal Policies From Data (3/4)
• From Prediction to Action — How to Learn Optimal Policies From Data (4/4)
• Fast AutoML with FLAML + Ray Tune
• Feature Store - why?
• Feature store - 핵심 개념
• Feature store - Basic Architecture
• Efficient Data Valuation with Exact Shapley Values
• MLOps-Basics
• [변수 선택] sklearn에 있는 mutual_info_classif , mutual_info_regression를 활용하여 변수 선택하기 (feature selection)
• Apache Airflow와 Amazon SageMaker Feature Store 연동하기
• Decision Making at Netflix
• What is an A/B Test?
• Creating Configurable Data Pre-Processing Pipelines by Combining Hydra and Sklearn
• AutoML Tutorial: TPS (September 2021)
• Machine Learning on Kubernetes : [Video]
• [Causal Inference KR] 스타트업에서의 인과추론
• Kaggle Solutions - The Most Comprehensive List of Kaggle Solutions and Ideas
• MLOps Basics
• Machine learning cheat sheet
• End-to-End Machine Learning Library
• 베이지안 인과 관계 추론 톺아보기
• A Brief Overview of Methods to Explain AI (XAI)
• InterpretML: Another Way to Explain Your Model
• The right way to compute your Shapley Values
• “My data drifted. What’s next?” How to handle ML model drift in production.
• 모두의 MLOps

Data Science

• 데이터는 차트가 아니라 돈이 되어야 한다
• 수강료 500만원 데이터 사이언스 커리큘럼을 대체하는 무료강의 15개 커리큘럼
• DODOMIRA님의 Data Analysis 블로그
• 데이터 입수 이상징후 탐지
• 네이버 파이낸스 - 재무제표 크롤링 | FinanceData
• Essence of linear algebra
• E-Mail 데이터 곱씹어보기
• Python_numpy_pandas_matplotlib 이해하기
• 2016 PyCon APAC - 너의 사진은 내가 지난 과거에 한 일을 알고 있다
• [FAQ] - Daum부동산 - DataFrame 행 추출과 컬럼으로 합치기
• gimmesilver님의 데이터 분석 블로그
• gimmesilver님의 데이터 분석 브런치
• datageek님의 데이터 분석 블로그
• PinkWink님의 데이터 분석 블로그
• NumPy Tutorial: Data analysis with Python
• K-MOOC Operation Research : Numpy Part #1
• K-MOOC Operation Research : Numpy Part #2
• Reproducible Data Analysis in Jupyter
• 에어브릿지 블로그 - Data science
• Analyze one year of radio station songs aired with SQL, Spark, Spotify, and Databricks
• 데이터 사이언스 스쿨
• Linear Algebra & Matrix Calculus
  • Math Tutorial I
  • Math Tutorial II
• Apache Spark - NewCircle Training
• Apache Spark for Data Science
• Effectively Using Matplotlib
• Kernel Density Estimation[커널밀도추정]에 대한 이해
• Mathpresso 머신 러닝 스터디 — 14. 밀도 추정[Density Estimation]
• 20. 데이터과학을 시작 할때 도움이 되는 것들 - 상
• Explained Visually
• Python for data analysis
• 스타트업 데이터분석 - 퍼널분석과 코호트분석
• Natural Korean Processor for Apache Spark
• 한글 NLP with Python
• 데이터를 얻으려는 노오오력
• Natural Language Processing with Quora
• 데이터 분석을 이용한 게임 고객 모델링 #4
• Exploratory Image Analysis
• Image Augmentation for Deep Learning using Keras and Histogram Equalization
• Facets: An Open Source Visualization Tool for Machine Learning Training Data
• 데이터과학을 시작할 때 도움되는 것들
• 데이터/통계 분석값에 대한 직관적 이해
• 노가다 없는 텍스트 분석을 위한 한국어 NLP - Pycon Korea 2017
• Data Wrangling with pandas Cheat Sheet
• Analyzing Twitter Data With Spark and Algebird
• Kaggle Data Science Bowl 2017 Data Preprocessing
• Pandas를 이용한 국제 상품 가격 데이터 분석
• Python Data Science Handbook
• 따라 하며 배우는 데이터 과학
• 문용준님의 Python Data Science Tutorial Code
• Python Seaborn Cheat Sheet For Statistical Data Visualization
• Numerical Tours of Data Sciences
• 빅데이터 : 샘플 양의 힘 [quantity over quality]
• The Probability and Statistics Cookbook
• 3Blue1Brown - Calculus, Linear Algebra
• Interactive Visualizations In Jupyter Notebook
• [데이터야놀자2107] 강남 출근길에 판교/정자역에 내릴 사람 예측하기
• How you can ditch PowerPoint and build better slides with Jupyter and Reveal.js
• Python Graph Gallery
• 베이지안 추론 - 1편
• Generating product usage data from scratch with Pandas
• A Data Science Workflow
• NumPy Exercises
• 파이썬 뉴스 텍스트 워드 클라우드
• Wrangling data with Pandas
• InSpace 딥러닝 관련 블로그
• 아주 심플한 블룸필터의 원리
• Conditional probability explained visually [Bayes' Theorem]
• 벡터, 행렬에 대한 미분
• Probability concepts explained
  • Introduction
  • Maximum likelihood estimation
  • Bayesian inference for parameter estimation
• 일자무식으로 데이터 사이언스 도전기
• pandas-profiling - Create HTML profiling reports from pandas DataFrame objects
• The Art of Effective Visualization of Multi-dimensional Data
• 제대로 시작하는 기초통계학
• Python Data Wrangling Tutorial: Cryptocurrency Edition
• Notes On Using Data Science & Artificial Intelligence To Fight For Something That Matters
• 확률변수를 이해하다
• matplotlib 한글폰트 사용하기
• Datascience-Interview-Questions for Korean
• A Beginner’s Guide to Data Engineering — Part I
• A Beginner’s Guide to Data Engineering — Part II
• A Beginner’s Guide to Data Engineering — The Series Finale
• Khan Academy - Linear Algebra
• Interactive Bokeh Tutorial Part 1
• Jupyter Notebook Tutorial: The Definitive Guide
• How to rewrite your SQL queries in Pandas, and more
• Common Patterns for Analyzing Data
• Data Visualization with Bokeh in Python Part One: Getting Started
• Data Visualization with Bokeh in Python, Part II: Interactions
• Data Visualization with Bokeh in Python, Part III: A Complete Dashboard
• Histograms and Density Plots in Python
• Visualizing Data with Pairs Plots in Python
• Analysing IPL Data to begin Data Analytics with Python
• Interactive Visualization of Australian Wine Ratings - Bokeh
• Seeing Theory - A visual introduction to probability and statistics
• K-MOOC: Operations Research with Python
• Visualize your data with Facets
• An Introduction to Altair
• 5 Quick and Easy Data Visualizations in Python with Code
• Boost your data science skills. Learn linear algebra
• Data Science. Intro
• 파이썬 X 스타벅스 매장 데이터와 지도 - pandas
• 파이썬 데이터 사이언스 Cheat Sheet: NumPy 기본
• 선형대수 - 한양대학교 이상화
• Data Exploration with Python, Part 1
• Data Exploration with Python, Part 2
• Data Exploration with Python, Part 3
• Fundamental Python Data Science Libraries: A Cheatsheet [Part 1/4]
• Fundamental Python Data Science Libraries: A Cheatsheet [Part 2/4]
• Introducing Dash
• Bokeh vs Dash — Which is the Best Dashboard Framework for Python?
• Creating Interactive Visualizations with Plotly’s Dash Framework
• Interactive, Web-Based Dashboards in Python - Dash
• Using Plotly’s Dash to deliver public sector decision support dashboards : [Video-1], [Video-2], [Video-3]
• ARGO Labs - Plotly Dash Tutorial
• Karnataka 2013 Election Results Visualized using Plotly’s Dash
• Visualizing Bitcoin prices moving averages using Dash
• Finding Bigfoot with Dash, Part 1
• Finding Bigfoot with Dash, Part 2
• Finding Bigfoot with Dash, Part 3
• How to Use Data Science to Understand What Makes Wine Taste Good : [Code]
• Visualize Your Data with Google Data Studio
• How to Use Statistics to Identify Outliers in Data
• 파이썬 데이터 분석 3종세트
• Fundamentals of Data Visualization
• Understanding Feature Engineering [Part 1] — Continuous Numeric Data
• Understanding Feature Engineering [Part 2] — Categorical Data
• Understanding Feature Engineering [Part 3] — Traditional Methods for Text Data
• Understanding Feature Engineering [Part 4] — Deep Learning Methods for Text Data
• 통계적 사고 워크샵
• Python For Data Science Training
• Data Science Simplified Part 1: Principles and Process
• Data Science Simplified Part 2: Key Concepts of Statistical Learning
• Data Science Simplified Part 3: Hypothesis Testing
• Data Science Simplified Part 4: Simple Linear Regression Models
• Data Science Simplified Part 5: Multivariate Regression Models
• Data Science Simplified Part 6: Model Selection Methods
• Data Science Simplified Part 7: Log-Log Regression Models
• Data Science Simplified Part 8: Qualitative Variables in Regression Models
• Data Science Simplified Part 9: Interactions and Limitations of Regression Models
• Data Science Simplified Part 10: An Introduction to Classification Models
• Data Science Simplified Part 11: Logistic Regression
• Tidying Datasets in Python
• Overview of Dash Python Framework from Plotly for building dashboards
• Handling Categorical Data in Python
• Learn More About Pandas By Building and Using a Weighted Average Function
• Let’s talk about NumPy — for Data Science Beginners
• Quick dive into Pandas for Data Science
• Introduction to Matplotlib — Data Visualization in Python
• The 10 Statistical Techniques Data Scientists Need to Master
• Statistics for people in a hurry
• On Average, You’re Using the Wrong Average: Geometric & Harmonic Means in Data Analysis
• On Average, You’re Using the Wrong Average — Part II
• Pandas and Tidy Data
• 깔끔한 데이터[Tidy data]
• FIFA World Cup 2018: A Data-Driven Approach to Ideal Team Line-Ups
• How to Generate FiveThirtyEight Graphs in Python
• Variability Methods in Statistics
• Understanding Descriptive Statistics
• Exploring and Visualizing Chicago Transit data using pandas and Bokeh — Part I [intro to pandas]
• Exploring and Visualizing Chicago Transit data using pandas and Bokeh — Part II [intro to Bokeh]
• Common data science pitfalls & how to avoid them!
• A Summary of Udacity A/B Testing Course
• Data science you need to know! A/B testing
• Linear algebra cheat sheet for deep learning
• 존이님의 확률과 통계 블로그
• 존이님의 선형대수학 블로그
• BigQuery와 Datalab을 사용해 데이터 분석하기
• Generating WordClouds in Python
• What App Descriptions Tell Us: Text Data Preprocessing in Python
• Dash for Beginners
• Beyond Interactive: Notebook Innovation at Netflix
• Python Plotting Basics — Simple Charts with Matplotlib, Seaborn, and Plotly
• [PyCon KR 2018] 땀내를 줄이는 Data와 Feature 다루기
• Transforming Skewed Data
• Pandas 10분 완성
• Python에서 데이터 시각화하는 다양한 방법
• Google Colab 사용하기
• The 10 Mining Techniques Data Scientists Need for Their Toolbox
• Pre-Modeling: Data Preprocessing and Feature Exploration in Python
• Julie Michelman - Pandas, Pipelines, and Custom Transformers
• Essential Math for Data Science — ‘Why’ and ‘How’
• Mathematics for Machine Learning: Linear Algebra
• MIT 18.06 Linear Algebra
• MIT - Probabilistic Systems Analysis and Applied Probability
• Harvard - Statistics 110: Probability
• A new plot theme for Matplotlib — Gadfly
• The ambiguity of p-value; What is it?
• Data Science Topics
• Introduction to Customer Segmentation in Python
• Demystifying crucial statistics in Python
• DataViz Cheatsheet
• Data Science Cheatsheet
• Mathematics for Machine Learning
• Git Version Control with Jupyter Notebooks
• How to Export Jupyter Notebooks into Other Formats
• How to Work with BIG Datasets on 16G RAM [+Dask]
• Your Ultimate Guide to Matplotlib
• Simpson’s Paradox: How to Prove Opposite Arguments with the Same Data
• How to use common workflows on Amazon SageMaker notebook instances
• The art of A/B testing
• Cookiecutter Data Science — Organize your Projects — Atom and Jupyter
• How to become a Data Scientist ?
• How to Setup Your JupyterLab Project Environment
• Creating Presentations with Jupyter Notebook
• Introducing Jupytext
• Learn Data Science in 3 Months by Siraj Raval
• Jupyter Notebook Cheat Sheet
• Awesome seaborn for Data Visualisation – Part 1
• Awesome seaborn for Data Visualization – Part 2
• Logging in Tensorboard with PyTorch [or any other library]
• 5 Key Factors to keep in mind while Optimizing Apache Spark in AWS[Part 1]
• 5 Key Factors to keep in mind while Optimising Apache Spark in AWS[Part 2]
• What’s new in Apache Spark 2.3 and Spark 2.4
• GeoPandas 101: Plot any data with a latitude and longitude on a map
• Kaggle Competition on Google Colab — how to easily import datasets and local files and access remote terminal
• Data Engineering 101
• 5 Critical Steps to Predictive Business Analytics
• Vectorized UDF: Scalable Analysis with Python and PySpark with Li Jin
• Colaboratory 사용하기
• Advanced sports visualization with Python, Matplotlib and Seaborn
• HandySpark: bringing pandas-like capabilities to Spark DataFrames
• Cleaning and Prepping Data with Python for Data Science — Best Practices and Helpful Packages
• Dash: A Beginner’s Guide
• Mapmaking with Plotly
• This will make you know how much you need to travel with Airbnb
• Data Visualization using Matplotlib
• Chartify - Python library that makes it easy for data scientists to create charts
• How to plot seismic activity with Anaconda and Jupyter Notebooks
• The Data Visualisation Catalogue
• Anatomy of Apache Spark Job
• Insights from Spark UI
• SciPy를 사용한 기초적인 검정
• T-test in python
• T-test
• Probability and Statistics for Data Science Part-1
• Matplotlib — Making data visualization interesting
• Estimating Probabilities with Bayesian Modeling in Python
• Exploratory Data Analysis [EDA] techniques for kaggle competition beginners
• A Beginner’s Guide to Plotting ‘FiveThirtyEight Like’ Visualizations
• Hypothesis Testing: how to determine significance
• Financial Times Visual Vocabulary
• Shareable Data Science with Kyso
• Jupyter Notebook Extensions : [번역]
• A short guide to using Docker for your data science environment
• Preprocessing with sklearn: a complete and comprehensive guide
• Effective Visualization of Multi-Dimensional Data — A Hands-on Approach
• An easy to use waterfall chart function for Python
• Introduction to Interactive Time Series Visualizations with Plotly in Python
• Docker for Data Science Without the Hassle
• Top 50 matplotlib Visualizations
• Tidying Up Pandas
• Bringing the best out of Jupyter Notebooks for Data Science
• How to Version Control Jupyter Notebooks
• PyParis 2018 - Jupytext: Edit Jupyter notebooks represented as Python scripts
• Optimizing Jupyter Notebooks - A Comprehensive Guide
• Advanced Jupyter Notebooks: A Tutorial
• Who Is Your Golden Goose?: Cohort Analysis
• How to A/B test without spending a dime
• The Next Level of Data Visualization in Python
• The Search for Categorical Correlation
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• Visual Studio Code로 편리한 Pylife!
• 베이지안 A/B 테스트
• Unleash the power of Jupyter Notebooks
• Bayesian A/B Testing with Python: the easy guide
• 데이터 사이언스를 공부하고 싶은 분들을 위한 글
• 조금 더 생각해보는 p-value
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• The Apache Spark™ Cost-Based Optimizer
• 장바구니를 든 데이터 사이언티스트
• Interactive Controls in Jupyter Notebooks
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• PyViz: Simplifying the Data Visualisation process in Python
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• Dunder Data - Pandas Tutorials
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• Blazer - Explore your data with SQL. Easily create charts and dashboards, and share them with your team.
• 과학적 방법과 실험 설계
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• 공공데이터 분석
• How to Automatically Import Your Favorite Libraries into IPython or a Jupyter Notebook
• 상관계수 총정리 끝판왕
• Plotly's Jupyterlab Chart Editor
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• A gentle introduction to Dash development and deployment
• Jupyter notebook 디버깅 그리고 qtconsole연결하기
• Jupyter Lab: Evolution of the Jupyter Notebook
• A General Guidance of Hypothesis Testing
• Data Visualization: An Intro to Plotly's Cufflinks
• 네트워크 분석기법을 활용한 게임 데이터 분석 #1
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• Set Your Jupyter Notebook up Right with this Extension
• Data Visualization - A practical introduction
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• 시각화 만들기, 이것만 알면 누구나 할 수 있다!
• Lesser Known Python Libraries for Data Science
• Interactive spreadsheets in Jupyter
• 멋지게 데이터 분석을 하려고 했는데 이론이 딸린다
• A Complete Guide to an Interactive Geographical Map using Python
• Plotting text and image vectors using t-SNE
• A Gentle Introduction to Interactive Geoplots With Plotly And MapBox
• How to: Folium for maps, heatmaps & time analysis
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• How to Build a Reporting Dashboard using Dash and Plotly
• Spatial Visualizations and Analysis in Python with Folium
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• 파이썬으로 스팀잇 형태소 분석하기 #1
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• The Jungle of Koalas, Pandas, Optimus and Spark
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• Data Science with Optimus. Part 2: Setting your DataOps Environment.
• A walkthrough of DVC
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• Pandas 기초 - cheat sheet 따라하기
• Introducing Spark-Select for MinIO Data Lakes
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• Amazon SageMaker notebooks now support Git integration for increased persistence, collaboration, and reproducibility
• Scalable Python Code with Pandas UDFs: A Data Science Application
• How To Find Probability From Probability Density Plots
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• Giving Your Algorithm a Spark
• Python을 이용한 콴다 리뷰 분석
• DeepNOL! - 빅데이터, 인공지능, 데이터과학 이야기
• An easy introduction to 3D plotting with Matplotlib
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• A guideline for basic use and installation of kubeflow in AWS
• Plotnine replication of Financial Times Visual Vocabulary; Inspired by Vega
• Panel: A high-level app and dashboarding solution for the PyData ecosystem.
• 그들이 AWS 위에서 데이터 파이프 라인을 운영하는 법
• 판다스 코드 속도 최적화를 위한 초보자 안내서
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• A Visual Intro to NumPy and Data Representation
• Introducing kepler.gl for Jupyter
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• How I built a spreadsheet app with Python to make data science easier
• 그들이 AWS 위에서 데이터 파이프라인을 운영하는 법
• 10 Simple hacks to speed up your Data Analysis in Python
• 2019년 pycon 튜토리얼 (Python으로 지리공간데이터 다루기) 실습 파일
• Guest Blog: How Virgin Hyperloop One reduced processing time from hours to minutes with Koalas
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• An introduction to plotnine.
• A Comprehensive Guide to the Grammar of Graphics for Effective Visualization of Multi-dimensional Data
• How to Create an Interactive Geographic Map Using Python and Bokeh
• Demystifying hypothesis testing with simple Python examples
• Visual Storytelling with Seaborn
• Exploratory Data Analysis: A Practical Guide and Template for Structured Data
• P-value Explained Simply for Data Scientists
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• Jovian: The platform for all your Data Science projects
• IPython Notebook Support is Finally Here for Visual Studio Code
• 타다 (TADA) 서비스의 데이터 웨어하우스 : 태초부터 현재까지
• Netflix Open Sources Polynote to Make Data Science Notebooks Better
• 4 Tips To Write Scalable Apache Spark Code
• Exploring your data with just 1 line of Python
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• Quickly Build and Deploy a Dashboard with Streamlit
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• Image processing using scikit image
• One Word of Code to Stop Using Pandas So Slowly
• Streamlit 101: An in-depth introduction
• Power up your Python Projects with Visual Studio Code
• How to Deploy a Streamlit App using an Amazon Free ec2 instance?
• 이왕이면 다홍 데이터
• Learn Metaflow in 10 mins — Netflix’s Python/R Framework for Data Science
• Voila를 사용해 Jupyter Notebook Dashboard 만들기
• O'reilly Strata Data Conference New York 2019 후기
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• Plotnine: Grammar of Graphics for Python
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• Comparing Rows Between Two Pandas DataFrames
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• VS CODE를 웹 상에 띄워놓고 어디서든 코딩하기
• Getting Started to Work With Jupyter Notebooks in Visual Studio Code
• Learn HiPlot in 6 mins — Facebook’s Python Library for Machine Learning Visualizations
• Version control with Jupyter Notebook
• Master Spark fundamentals & optimizations
• Pandas 100 tricks
• Useful Python libraries for Data Science
• Jupyter Notebooks in the IDE
• Spark+DynamoDB - Plug-and-play implementation of an Apache Spark custom data source for AWS DynamoDB
• [KR] 파이썬 웹어플리케이션 맛보기 (feat. Streamlit)
• Qubole Sparklens tool for performance tuning Apache Spark
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• A visual debugger for Jupyter
• Jupyter is now a full-fledged IDE
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• Best practices for successfully managing memory for Apache Spark applications on Amazon EMR
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• How to interpret p-value with COVID-19 data
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• How to build your Machine Learning App in 3 simple steps
• How to write Web apps using simple Python for Data Scientists?
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• NLP visualizations for clear, immediate insights into text data and outputs
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• Beyond “tidy”: Plotly Express now accepts wide-form and mixed-form data
• 10 Steps to Setup a Comprehensive Data Science Workspace with VSCode on Windows
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• Spark 3.0에 새로 추가된 기능 소개 및 설명
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• apache hudi 적용해서 aws 에서 glue metastore 기반 테이블만들기
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• [올바�