anusha0409's Stars
jwasham/coding-interview-university
A complete computer science study plan to become a software engineer.
donnemartin/system-design-primer
Learn how to design large-scale systems. Prep for the system design interview. Includes Anki flashcards.
practical-tutorials/project-based-learning
Curated list of project-based tutorials
papers-we-love/papers-we-love
Papers from the computer science community to read and discuss.
DopplerHQ/awesome-interview-questions
:octocat: A curated awesome list of lists of interview questions. Feel free to contribute! :mortar_board:
josephmisiti/awesome-machine-learning
A curated list of awesome Machine Learning frameworks, libraries and software.
prakhar1989/awesome-courses
:books: List of awesome university courses for learning Computer Science!
floodsung/Deep-Learning-Papers-Reading-Roadmap
Deep Learning papers reading roadmap for anyone who are eager to learn this amazing tech!
ZuzooVn/machine-learning-for-software-engineers
A complete daily plan for studying to become a machine learning engineer.
eugeneyan/applied-ml
📚 Papers & tech blogs by companies sharing their work on data science & machine learning in production.
sebastianruder/NLP-progress
Repository to track the progress in Natural Language Processing (NLP), including the datasets and the current state-of-the-art for the most common NLP tasks.
checkcheckzz/system-design-interview
System design interview for IT companies
jbhuang0604/awesome-computer-vision
A curated list of awesome computer vision resources
keon/awesome-nlp
:book: A curated list of resources dedicated to Natural Language Processing (NLP)
oxford-cs-deepnlp-2017/lectures
Oxford Deep NLP 2017 course
vdumoulin/conv_arithmetic
A technical report on convolution arithmetic in the context of deep learning
chiphuyen/stanford-tensorflow-tutorials
This repository contains code examples for the Stanford's course: TensorFlow for Deep Learning Research.
py-why/dowhy
DoWhy is a Python library for causal inference that supports explicit modeling and testing of causal assumptions. DoWhy is based on a unified language for causal inference, combining causal graphical models and potential outcomes frameworks.
udacity/machine-learning
Content for Udacity's Machine Learning curriculum
BedirT/ACM-ICPC-Preparation
ACM-ICPC Preparation Guide
indy256/codelibrary
:gem:Collection of algorithms and data structures
nicodjimenez/lstm
Minimal, clean example of lstm neural network training in python, for learning purposes.
vineetjohn/daily-coding-problem
Solutions to problems sent by dailycodingproblem.com
py-why/causal-learn
Causal Discovery in Python. It also includes (conditional) independence tests and score functions.
sharkdp/great-puzzles
A curated list of great puzzles
erdogant/bnlearn
Python package for Causal Discovery by learning the graphical structure of Bayesian networks. Structure Learning, Parameter Learning, Inferences, Sampling methods.
DataForScience/Causality
syanga/pycit
(Conditional) Independence testing & Markov blanket feature selection using k-NN mutual information and conditional mutual information estimators. Supports continuous, discrete, and mixed data, as well as multiprocessing.
benedekrozemberczki/resolutions-2019
A list of data mining and machine learning papers that I implemented in 2019.
uhlerlab/conditional_independence
Parametric and non-parametric conditional independence testing.