gu-raime

applied-ml

📚 Papers & tech blogs by companies sharing their work on data science & machine learning in production.

awesome-machine-learning

A curated list of awesome Machine Learning frameworks, libraries and software.

cam_face_recognition_app

Face Recognition Using Cam Streaming

Dados-Antropometricos-e-Ergonomia-sobre-Equipamentos-na-Pratica-Odontologica-

Análise de dados antropométricos dos estudantes de graduação do curso de Odontologia da Universidade de São Paulo

data-science-ipython-notebooks

Data science Python notebooks: Deep learning (TensorFlow, Theano, Caffe, Keras), scikit-learn, Kaggle, big data (Spark, Hadoop MapReduce, HDFS), matplotlib, pandas, NumPy, SciPy, Python essentials, AWS, and various command lines.

deep-learning-with-python-notebooks

Jupyter notebooks for the code samples of the book "Deep Learning with Python"

Dental-Informatics

🦷 This repository was created in order to bring to students, teachers and dentistry professionals, an overview related to the topic of usage of informatics in dentistry, also known as Dental Informatics

dental.informatics.org

edX-6.86x-machine-learning

Machine Learning with Python (url: https://courses.edx.org/courses/course-v1:MITx+6.86x+1T2020/course/)

text_clustering

k-means text clustering using cosine similarity.

gu-raime/Dados-Antropometricos-e-Ergonomia-sobre-Equipamentos-na-Pratica-Odontologica-

Análise de dados antropométricos dos estudantes de graduação do curso de Odontologia da Universidade de São Paulo

gu-raime/Dental-Informatics

🦷 This repository was created in order to bring to students, teachers and dentistry professionals, an overview related to the topic of usage of informatics in dentistry, also known as Dental Informatics

gu-raime/edX-6.86x-machine-learning

Machine Learning with Python (url: https://courses.edx.org/courses/course-v1:MITx+6.86x+1T2020/course/)

gu-raime/text_clustering

k-means text clustering using cosine similarity.

gu-raime/applied-ml

📚 Papers & tech blogs by companies sharing their work on data science & machine learning in production.

gu-raime/awesome-machine-learning

A curated list of awesome Machine Learning frameworks, libraries and software.

gu-raime/cam_face_recognition_app

Face Recognition Using Cam Streaming

gu-raime/data-science-ipython-notebooks

Data science Python notebooks: Deep learning (TensorFlow, Theano, Caffe, Keras), scikit-learn, Kaggle, big data (Spark, Hadoop MapReduce, HDFS), matplotlib, pandas, NumPy, SciPy, Python essentials, AWS, and various command lines.

gu-raime/deep-learning-with-python-notebooks

Jupyter notebooks for the code samples of the book "Deep Learning with Python"

gu-raime/dental.informatics.org

gu-raime/face_recognition

The world's simplest facial recognition api for Python and the command line

gu-raime/gitflow_learnign

Git flow learning on gitkraken

gu-raime/gu-raime

My profile.

gu-raime/gu-raime.github.io

My personal website

gu-raime/Machine_Learning_and_Deep_Learning

gu-raime/MITx-6.86x

MITx - 6.86x - Machine Learning with Python-From Linear Models to Deep Learning

gu-raime/mitx-machine-learning-with-python-from-linear-models-to-deep-learning

Course 4 of 4 in the MITx MicroMasters program in Statistics and Data Science

gu-raime/MITx_6.86x_Machine_Learning_with_Python-From_Linear_Models_to_Deep_Learning_Fall_2020

Welcome to 6.86x Machine Learning with Python–From Linear Models to Deep Learning. Machine learning methods are commonly used across engineering and sciences, from computer systems to physics. Moreover, commercial sites such as search engines, recommender systems (e.g., Netflix, Amazon), advertisers, and financial institutions employ machine learning algorithms for content recommendation, predicting customer behavior, compliance, or risk. As a discipline, machine learning tries to design and understand computer programs that learn from experience for the purpose of prediction or control. In this course, you will learn about principles and algorithms for turning training data into effective automated predictions. We will cover: Representation, over-fitting, regularization, generalization, VC dimension; Clustering, classification, recommender problems, probabilistic modeling, reinforcement learning; On-line algorithms, support vector machines, and neural networks/deep learning. You will be able to: Understand principles behind machine learning problems such as classification, regression, clustering, and reinforcement learning Implement and analyze models such as linear models, kernel machines, neural networks, and graphical models Choose suitable models for different applications Implement and organize machine learning projects, from training, validation, parameter tuning, to feature engineering You will implement and experiment with the algorithms in several Python projects designed for different practical applications. You will expand your statistical knowledge to not only include a list of methods, but also the mathematical principles that link these methods together, equipping you with the tools you need to develop new ones.

gu-raime/notebooks

Notebooks using the Hugging Face libraries 🤗

gu-raime/Python

All Algorithms implemented in Python

gu-raime/pytorch3d

PyTorch3D is FAIR's library of reusable components for deep learning with 3D data

gu-raime/sheetjs

:green_book: SheetJS Community Edition -- Spreadsheet Data Toolkit

gu-raime/shock

Free business application template, front & dashborad, build on vuetify

gu-raime/size-limit

Calculate the real cost to run your JS app or lib to keep good performance. Show error in pull request if the cost exceeds the limit.

gu-raime/testPrediction

gu-raime/website