https://github.com/ashishpatel26/star-collection-of-machine-learning

Original Author link : https://github.com/charlesliucn/stars-collection
https://github.com/ashishpatel26/star-collection-of-machine-learning

deep-learning deep-learning-tutorial deep-learning-tutorials dl-tutorial machine-learning machine-learning-algorithms machine-learning-tutorials machinelearning ml ml-tutorial tutorials

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Original Author link : https://github.com/charlesliucn/stars-collection

• Host: GitHub
• URL: https://github.com/ashishpatel26/star-collection-of-machine-learning
• Owner: ashishpatel26
• License: mit
• Created: 2019-05-31T17:59:15.000Z (about 6 years ago)
• Default Branch: master
• Last Pushed: 2019-05-31T17:59:27.000Z (about 6 years ago)
• Last Synced: 2025-01-20T05:10:59.912Z (5 months ago)
• Topics: deep-learning, deep-learning-tutorial, deep-learning-tutorials, dl-tutorial, machine-learning, machine-learning-algorithms, machine-learning-tutorials, machinelearning, ml, ml-tutorial, tutorials
• Homepage:
• Size: 905 KB
• Stars: 2
• Watchers: 2
• Forks: 2
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

        
# My Starred Repositories [![License](https://img.shields.io/badge/license-MIT-blue.svg)](./LICENSE)

This repository contains my starred repositories grouped into different categories.

**Last Update:** Feb. 18th, 2019

**Author:** [@charlesliucn](https://github.com/charlesliucn)

**Newest Stars** see [here](https://nbviewer.jupyter.org/github/charlesliucn/stars-collection/blob/master/GitHub-Links.pdf).

**Keras Notes** see [here](https://github.com/charlesliucn/stars-collection/blob/master/keras_notes.md)

* * *

## Table of Contents

### [1. Programming](#programming)

  - [1.1 Tutorials & Cheatsheets](#tutorialscheatsheets)

  - [1.2 Source Code for Books](#sourcecodeforbooks)

  - [1.3 Coding Websites and contests](#codingcontest)

### [2. Machine Learning & Deep Learning](#mldl)

  - [2.1 Machine Learning](#ml)

  - [2.2 Deep Learning](#dl)

  - [2.3 Reinforcement Learning](#rl)

  - [2.4 Toolkit or Library](#toolkit)

  - [2.5 Generative Adversial Networks](#GAN)

  - [2.6 NLP(Natural Language Processing)](#NLP)

  - [2.7 Speech Recognition](#SR)

  - [2.8 Data Mining](#DM)

### [3. Data Science](#ds)

### [4. Github Pages](#gp)

### [5. Technology](#tech)

### [6. Job and Career](#job)

* * *

* * *

##  1. Programming

###  1.1 Tutorials & Cheatsheets

+ [Awesome Cheatsheet](https://github.com/detailyang/awesome-cheatsheet)

+ [Awesome Data Science](https://github.com/bulutyazilim/awesome-datascience)

+ [Awesome Programming Books](https://github.com/jobbole/awesome-programming-books)

+ [Awesome Python](https://github.com/vinta/awesome-python)

+ [Awesome Python in Chinese](https://github.com/jobbole/awesome-python-cn)

+ [Data Structure and Algorithms in Python](https://github.com/keon/algorithms)

+ [Free Programming Books](https://github.com/justjavac/free-programming-books-zh_CN)

+ [Practical Programming Books](https://github.com/EZLippi/practical-programming-books)

+ [Python: Algorithms Implementation](https://github.com/prakhar1989/Algorithms)

+ [Python: Gensim NLP Toolkit](https://github.com/RaRe-Technologies/gensim)

+ [Python: Pandas Cookbook](https://github.com/jvns/pandas-cookbook)

+ [Python Programming: Games and Problems](https://github.com/norvig/pytudes)

+ [Python Programming](https://github.com/xxg1413/python)

+ [Ubuntu Cheatsheet](https://github.com/Aaronontheweb/ubuntu-cheatsheet)

###  1.2 Source Code for Books

+ [Algorithms: 4th Edition](https://github.com/kevin-wayne/algs4)

+ [Algorithms (4th edition)](https://github.com/jimmysuncpt/Algorithms)

+ [Building Machine Learning Systems with Python](https://github.com/luispedro/BuildingMachineLearningSystemsWithPython)

+ [Introduction to Machine Learning with Python](https://github.com/amueller/introduction_to_ml_with_python)

+ [Machine Learning for Hackers](https://github.com/johnmyleswhite/ML_for_Hackers)

+ [Machine Learning With TensorFlow](https://github.com/BinRoot/TensorFlow-Book)

+ [Neural Networks and Deep Learning](https://github.com/mnielsen/neural-networks-and-deep-learning)

+ [Pattern Recognition and Machine Learning: Python](https://github.com/ctgk/PRML)

+ [The Elements of Statistical Learning: I](https://github.com/szcf-weiya/ESL-CN)

+ [The Elements of Statistical Learning: II](https://github.com/ajtulloch/Elements-of-Statistical-Learning)

+ [Reinforcement Learning: An Introduction](https://github.com/ShangtongZhang/reinforcement-learning-an-introduction)

###  1.3 Contests for Programming

+ [Algorithm Solutions](https://github.com/intfloat/AlgoSolutions)

+ [Leetcode Solutions](https://github.com/kamyu104/LeetCode)

* * *

##  2. Machine Learning & Deep Learning

###  2.1 Machine Learning

+ [Awesome Machine Learning](https://github.com/josephmisiti/awesome-machine-learning)

+ [Awesome Machine Learning in Chinese](https://github.com/jobbole/awesome-machine-learning-cn)

+ [Awesome Machine Learning on Source Code](https://github.com/src-d/awesome-machine-learning-on-source-code)

+ [Dive into Machine Learning](https://github.com/hangtwenty/dive-into-machine-learning)

+ [Machine Learning and Deep Learning Tutorials](https://github.com/ujjwalkarn/Machine-Learning-Tutorials)

+ [Machine Learning and Pattern Recognition Tutorials](https://github.com/rasbt/pattern_classification)

+ [Machine Learning Algorithms](https://github.com/rushter/MLAlgorithms)

+ [Machine Learning Basic](https://github.com/wepe/MachineLearning)

+ [Machine Learning Basic Algorithms](https://github.com/X-Brain/MachineLearning)

+ [Machine Learning Cheatsheet](https://github.com/soulmachine/machine-learning-cheat-sheet)

+ [Machine Learning CookBook](https://github.com/nfmcclure/tensorflow_cookbook)

+ [Machine Learning Experiments](https://github.com/jiqizhixin/ML-Tutorial-Experiment)

+ [Machine Learning for Software Engineers](https://github.com/ZuzooVn/machine-learning-for-software-engineers)

+ [Machine Learning Guide for Beginners](https://github.com/humphd/have-fun-with-machine-learning)

+ [Machine Learning in Action](https://github.com/apachecn/MachineLearning)

+ [Machine Learning Mindmap](https://github.com/dformoso/machine-learning-mindmap)

+ [Machine Learning Notes](https://github.com/roboticcam/machine-learning-notes)

+ [Machine Learning Tutorials: Python3](https://github.com/ethen8181/machine-learning)

+ [Machine Learning Tutorials: Morvan](https://github.com/MorvanZhou/tutorials)

+ [Machine Learning Udacity Course](https://github.com/udacity/machine-learning)

+ [Machine Learning With R](https://github.com/kaushikb258/Machine-Learning-With-R)

+ [Python Machine Learning Book](https://github.com/rasbt/python-machine-learning-book)

+ [State of the Art Results for Machine Learning Problems](https://github.com/RedditSota/state-of-the-art-result-for-machine-learning-problems)

###  2.2 Deep Learning

+ [AI Cheatsheet](https://github.com/kailashahirwar/cheatsheets-ai)

+ [Awesome Deep Learning](https://github.com/ChristosChristofidis/awesome-deep-learning)

+ [Awesome Deep Learning Papers](https://github.com/terryum/awesome-deep-learning-papers)

+ [Deep Learning Book (Chinese)](https://github.com/exacity/deeplearningbook-chinese)

+ [Deep Learning for Java](https://github.com/deeplearning4j/deeplearning4j)

+ [Deep Learning Javascript](https://github.com/karpathy/convnetjs)

+ [Deep Learning Keras and Tensorflow](https://github.com/leriomaggio/deep-learning-keras-tensorflow)

+ [Deep Learning Papers](https://github.com/sbrugman/deep-learning-papers)

+ [Deep Learning Papers Reading Roadmap](https://github.com/songrotek/Deep-Learning-Papers-Reading-Roadmap)

+ [Deep Learning Papers and Notes](https://github.com/dennybritz/deeplearning-papernotes)

+ [Deep Learning Project I](https://github.com/Spandan-Madan/DeepLearningProject)

+ [Deep Learning Project II](https://github.com/priya-dwivedi/Deep-Learning)

+ [Deep Learning Tutorials](https://github.com/lisa-lab/DeepLearningTutorials)

+ [Deep Learning With Python Notebooks](https://github.com/fchollet/deep-learning-with-python-notebooks)

+ [Deep Learning With 21 examples](https://github.com/hzy46/Deep-Learning-21-Examples)

+ [Fast.ai Courses](https://github.com/fastai/courses)

+ [Fundamentals of Machine Learning and Deep Learning](https://github.com/ageron/handson-ml)

+ [Neural Networks for Beginners](https://github.com/AILabUSiena/NeuralNetworksForBeginners)

+ [Simplified Deep Learning](https://github.com/exacity/simplified-deeplearning)

+ [Solutions to deeplearning.ai](https://github.com/Yukong/Deeplearning.ai-Solutions)

+ [Qix: Machine Learning, Deep Learning](https://github.com/ty4z2008/Qix)

+ [Udacity: Deep-Learning Course](https://github.com/thushv89/udacity_deeplearning_complete)

###  2.3. Reinforcement Learning

+ [Awesome Reinforcement Learning](https://github.com/aikorea/awesome-rl)

+ [Reinforcement Learning I](https://github.com/dennybritz/reinforcement-learning)

+ [Reinforcement Learning II](https://github.com/rlcode/reinforcement-learning)

###  2.4 Toolkit or Library

+ [Apache Hadoop](https://github.com/apache/hadoop)

+ [Apache Spark](https://github.com/apache/spark)

+ [Caffe](https://github.com/BVLC/caffe)

+ [Caffe2](https://github.com/caffe2/caffe2)

+ [CNTK](https://github.com/Microsoft/CNTK)

+ [Deep Learning for Windows 10](https://github.com/philferriere/dlwin)

+ [GPU Training](https://github.com/NVIDIA/DIGITS)

+ [Keras](https://github.com/fchollet/keras)

+ [MXNet/Gluon](https://github.com/mli/gluon-tutorials-zh)

+ [PyTorch](https://github.com/pytorch/pytorch)

  - [Awesome PyTorch List](https://github.com/bharathgs/Awesome-pytorch-list)

  - [PyTorch Examples](https://github.com/pytorch/examples)

  - [PyTorch Tutorials](https://github.com/hunkim/PyTorchZeroToAll)

  - [PyTorch Tutorials](https://github.com/yunjey/pytorch-tutorial)

+ [Scikit-Learn](https://github.com/scikit-learn/scikit-learn)

+ [Tensorflow](https://github.com/tensorflow/tensorflow)

  - [Tensorboard](https://github.com/tensorflow/tensorboard)

  - [TensorFlow Examples](https://github.com/aymericdamien/TensorFlow-Examples)

  - [Tensorflow Experiments](https://github.com/jtoy/awesome-tensorflow)

  - [Tensorflow in Chinese](https://github.com/jikexueyuanwiki/tensorflow-zh)

  - [Tensorflow models](https://github.com/tensorflow/models)

  - [Tensorflow Practices](https://github.com/vahidk/EffectiveTensorflow)

  - [Tensorflow Tutorials: Morvan](https://github.com/MorvanZhou/Tensorflow-Tutorial)

  - [TensorFlow Tutorials: Nlintz](https://github.com/nlintz/TensorFlow-Tutorials)

  - [Tensorflow Tutorials: Pkmital](https://github.com/pkmital/tensorflow_tutorials)

  - [Tensorflow Tutorials: Stanford](https://github.com/chiphuyen/stanford-tensorflow-tutorials)

  - [Tensorflow With Latest Paper](https://github.com/NickShahML/tensorflow_with_latest_papers)

  - [Tensorflow Workshops](https://github.com/tensorflow/workshops)

  - [tflearn](https://github.com/tflearn/tflearn)

+ [Theano](https://github.com/Theano/Theano)

+ [TuriCreate](https://github.com/apple/turicreate)

###  2.5 Generative Adversial Networks

+ [Bayes GAN](https://github.com/andrewgordonwilson/bayesgan)

+ [Collection of GANs](https://github.com/hindupuravinash/the-gan-zoo)

+ [Introduction to GAN](https://github.com/AYLIEN/gan-intro)

+ [Improved WGANs: Pytorch](https://github.com/caogang/wgan-gp)

+ [Imporved WGANs: Tensorflow](https://github.com/igul222/improved_wgan_training)

+ [GAN models](https://github.com/wiseodd/generative-models)

+ [Generative Adversial Networks](https://github.com/YadiraF/GAN)

+ [Leak GAN](https://github.com/CR-Gjx/LeakGAN)

+ [Text GAN I](https://github.com/ankitkv/TextGAN)

+ [Text GAN II](https://github.com/AustinStoneProjects/TextGAN)

+ [RNN + GAN](https://github.com/amirbar/rnn.wgan)

+ [Wasserstein GANs](https://github.com/keon/text-wgan)

###  2.6 NLP(Natural Language Processing)

+ [Awesome NLP](https://github.com/keon/awesome-nlp)

+ [Awesome Chinese NLP](https://github.com/crownpku/Awesome-Chinese-NLP)

+ [Chinese natural language processing: Fudan University](https://github.com/FudanNLP/fnlp)

+ [CMU NN4NLP](https://github.com/neubig/nn4nlp2017-code)

+ [NLP Tutorial](https://github.com/neubig/nlptutorial)

+ [NLP Shell Tutorial](https://github.com/HIT-SCIR/scir-training-day)

+ [Deep Learning for NLP Resources](https://github.com/andrewt3000/DL4NLP)

+ [Oxford NLP Lectures](https://github.com/oxford-cs-deepnlp-2017/lectures)

+ [Python and Cython for NLP](https://github.com/explosion/spaCy)

+ [Speech and Natural Language Processing](https://github.com/edobashira/speech-language-processing)

+ **Language Modelling**

  - [1 Billion Word Benchmark](https://github.com/ciprian-chelba/1-billion-word-language-modeling-benchmark)

  - [char-RNN](https://github.com/karpathy/char-rnn)

  - [Exploring the limits of language modeling](https://github.com/rafaljozefowicz/lm)

  - [F-LSTM G-LSTM Language Modeling](https://github.com/okuchaiev/f-lm)

  - [Gated CNN](https://github.com/anantzoid/Language-Modeling-GatedCNN)

  - [LSTM: subword units](https://github.com/claravania/subword-lstm-lm/branches)

  - [LSTM cell Test by language modeling](https://github.com/asahi417/LSTMCell)

  - [Recurrent Highway Networks](https://github.com/julian121266/RecurrentHighwayNetworks)

  - [RNN: word-level](https://github.com/hunkim/word-rnn-tensorflow)

  - [RNN: character-level](https://github.com/sherjilozair/char-rnn-tensorflow)

  - [State of the art Language model](https://github.com/okuchaiev/f-lm)

+ **Text Generation**

  - [RNN Text Generation](https://github.com/spiglerg/RNN_Text_Generation_Tensorflow)

  - [Word Level Text Generation: RNN](https://github.com/rdcolema/word-level-rnn-for-text-generation)

  - [Word Level Text Generation using Keras](https://github.com/vlraik/word-level-rnn-keras)

+ **Toolkit**

  - [XenC: Data Selection for Language Processing](https://github.com/antho-rousseau/XenC)

+ **Word Clustering**

  - [Brown Clustering](https://github.com/mheilman/tan-clustering)

+ **Text Processing**

  - [Text Classifier](https://github.com/richliao/textClassifier)

  - [Compute Distance Between Sequences](https://github.com/orsinium/textdistance)

+ **Word Embedding/Word2Vec**

  - [Fast Text](https://github.com/facebookresearch/fastText)

  - [Four word2vec methods in Python](https://github.com/deborausujono/word2vecpy)

  - [GloVe Algorithm I: Tensorflow](https://github.com/GradySimon/tensorflow-glove)

  - [GloVe Algorithm II: Simple Python](https://github.com/maciejkula/glove-python)

  - [GloVe Algorithm III: Stanford Version](https://github.com/stanfordnlp/GloVe)

  - [Google word2vec](https://github.com/klb3713/word2vec)

  - [Intrinsic Evaluation](https://github.com/ytsvetko/qvec)

  - [ Multilingual Unsupervised or Supervised word Embeddings](https://github.com/facebookresearch/MUSE)

  - [Skip-Gram and CBOW I](https://github.com/dav/word2vec)

  - [Skip-Gram and CBOW II](https://github.com/deborausujono/word2vecpy)

  - [Skip-Gram and Negative Sampling](https://github.com/tscheepers/word2vec)

  - [Skip-Gram Algorithm: Pytorch](https://github.com/fanglanting/skip-gram-pytorch)

  - [Word Clustering: mkcls](https://github.com/clab/mkcls)

###  2.7 Speech Recognition

+ [Baidu Deep Speech](https://github.com/mozilla/DeepSpeech)

+ [Baidu Deep Speech 2](https://github.com/SeanNaren/deepspeech.pytorch)

+ [End-to-end Automatic Speech Recognition](https://github.com/zzw922cn/Automatic_Speech_Recognition)

+ [Framework for Detection Evaluation](https://github.com/usnistgov/F4DE)

+ [Kaldi IO for Python](https://github.com/vesis84/kaldi-io-for-python)

+ [Kaldi Toolkit](https://github.com/kaldi-asr/kaldi)

+ [Spoken Language Identification](https://github.com/YerevaNN/Spoken-language-identification)

+ [Tensorflow for Speech Recognition](https://github.com/pannous/tensorflow-speech-recognition)

+ [Voice Web: Speech Data Collection](https://github.com/mozilla/voice-web)

+ [Wavenet: End-to-end ASR](https://github.com/buriburisuri/speech-to-text-wavenet)

###  2.8 Data Mining

+ [Web Data Mining](https://github.com/clips/pattern)

* * *

##  3. Data Science

+ [Business Analytics](https://github.com/ethen8181/Business-Analytics)

+ [Data Analysis and Machine Learning](https://github.com/rhiever/Data-Analysis-and-Machine-Learning-Projects)

+ [Data Science Blogs](https://github.com/thedataincubator/data-science-blogs)

+ [Data Science Ipython Notebooks](https://github.com/donnemartin/data-science-ipython-notebooks)

+ [Data Science With Python](https://github.com/ujjwalkarn/DataSciencePython)

+ [FullStack Data Engineer](https://github.com/Honlan/fullstack-data-engineer)

+ [Python Data Science Handbook](https://github.com/jakevdp/PythonDataScienceHandbook)

+ **Competition**

  - [Awesome Public DataSet](https://github.com/awesomedata/awesome-public-datasets)

  - [Tianchi Competition](https://github.com/Jessicamidi/IJCAI17_Tianchi_Rank4)

  - [Zhihu Competition](https://github.com/chenyuntc/PyTorchText)

  - [Kaggle](https://github.com/apachecn/kaggle)

  - [ML/DL Competition List](https://github.com/PPshrimpGo/CompetitionList)

* * *

##   4. Github Pages

+ [Emoji Cheatsheet](https://github.com/WebpageFX/emoji-cheat-sheet.com)

+ [GitHub Profile Summary](https://github.com/tipsy/github-profile-summary)

+ [Icons for Github Pages or Github Markdown](https://github.com/edent/SuperTinyIcons)

+ [Hexo Themes](https://github.com/hexojs/hexo)

  - [Academic Pages](https://github.com/academicpages/academicpages.github.io)

  - [cnfeat.com](https://github.com/cnfeat/cnfeat.github.io)

  - [Rvlasveld](https://github.com/rvlasveld/rvlasveld.github.io)

+ [Icons for Github Pages or Github Markdown](https://github.com/edent/SuperTinyIcons)

* * *

##  5. Technology

+ [Alibaba Technology: Hangzhou Yunqi](https://github.com/Alibaba-Technology/hangzhouYunQi2017ppt)

+ [Awesome Github Wechat App](https://github.com/opendigg/awesome-github-wechat-weapp)

+ [Awesome Technology](https://github.com/ngEdmundas/awesome-technology/wiki)

+ [Awesome Wechat App](https://github.com/justjavac/awesome-wechat-weapp)

+ [Music Programming Language](https://github.com/alda-lang/alda)

+ [NIPS 2017 Resources](https://github.com/hindupuravinash/nips2017)

+ [Papers on Computer Science](https://github.com/papers-we-love/papers-we-love)

+ **Interesting Applications**

	- [shadowsocks windows](https://github.com/shadowsocks/shadowsocks-windows)

	- [qrcode generator](https://github.com/sylnsfar/qrcode)

* * *

##   6. Job and Career

+ [Awesome Interview Questions](https://github.com/MaximAbramchuck/awesome-interview-questions)

+ [Best Resume Ever](https://github.com/salomonelli/best-resume-ever)

+ [Coding Interview](https://github.com/Wang-Jun-Chao/coding-interviews)

+ [Coding Interview University](https://github.com/jwasham/coding-interview-university)

+ [Every Programmer should know](https://github.com/mr-mig/every-programmer-should-know)

+ [Latex Templates Collection](https://github.com/cmichi/latex-template-collection)

+ [Technical Interview](https://github.com/andreis/interview)

+ [Technical Interview Handbook](https://github.com/yangshun/tech-interview-handbook)

+ [Technical Interview Notebook](https://github.com/CyC2018/Interview-Notebook)

+ [The Art of Programming: Interview and Algorithms](https://github.com/julycoding/The-Art-Of-Programming-By-July)

+ **Courses**

	- [Coursera](https://github.com/intfloat/coursera)

	- [Self-Taught Computer Science](https://github.com/ossu/computer-science)

	- [Deep Learning Cousera](https://github.com/Kulbear/deep-learning-coursera)