Digest ML/NLP/CV/CG reading materials

[utensil]

ML Top:

• [ ] Deep Learning School 2016: Individual Talks
• [ ] Online Course on Neural Networks
• [ ] https://github.com/fchollet/keras-resources
• [ ] http://scikit-learn.org/stable/user_guide.html
• [ ] https://github.com/lamblin/bayareadlschool
• [ ] http://deeplearning.net/datasets
• [ ] https://github.com/alrojo/tensorflow-tutorial
• [ ] Deep Learning Summer School, Montreal 2016
• [ ] Unsupervised Feature Learning and Deep Learning
• [ ] CS224d: Deep Learning for Natural Language Processing
• [ ] CS231n: Convolutional Neural Networks for Visual Recognition
• [ ] https://github.com/baidu-research/ba-dls-deepspeech
• [ ] https://github.com/tiny-dnn/tiny-dnn
• [ ] https://github.com/dennybritz/reinforcement-learning
• [ ] https://github.com/Tetrachrome/subpixel
• [ ] https://github.com/mxgmn/WaveFunctionCollapse
• [ ] https://github.com/SullyChen/Nvidia-Autopilot-TensorFlow
• [ ] https://github.com/thoughtfulml/examples-in-python
• [ ] http://statweb.stanford.edu/~tibs/ElemStatLearn/
• [ ] https://github.com/johnmyleswhite/ML_for_Hackers
• [ ] http://lamda.nju.edu.cn/weixs/project/CNNTricks/CNNTricks.html
• [ ] https://github.com/ZuzooVn/machine-learning-for-software-engineers
• [ ] https://github.com/rushter/MLAlgorithms
• [ ] http://www.scipy-lectures.org/advanced/image_processing/index.html
• [ ] https://zhuanlan.zhihu.com/p/22129946
• [ ] http://conflict.lshtm.ac.uk/index.htm
• [ ] https://arxiv.org/abs/1404.7828
• [ ] http://www.jeremydjacksonphd.com/category/deep-learning/
• [ ] http://distill.pub/2016/misread-tsne/
• [ ] http://playground.tensorflow.org/
• [ ] http://projector.tensorflow.org/
• [ ] http://ai.berkeley.edu/home.html
• [ ] https://en.wikipedia.org/wiki/Topological_data_analysis
• [ ] https://www.youtube.com/subscription_manager
• [ ] http://rll.berkeley.edu/deeprlcourse/#syllabus ( https://zhuanlan.zhihu.com/p/24721292 )
• [ ] https://deepmind.com/blog/wavenet-generative-model-raw-audio/
• [ ] https://github.com/tensorflow/magenta/blob/master/magenta/reviews/pixelrnn.md
• [ ] https://gist.github.com/shagunsodhani/e741ebd5ba0e0fc0f49d7836e30891a7
• [ ] https://deepmind.com/blog/differentiable-neural-computers/
• [ ] https://deepmind.com/blog/deepmind-round-up-2016/
• [ ] https://github.com/phreeza/keras-GAN
• [ ] https://github.com/dustinvtran/ml-videos
• [ ] https://github.com/oxford-cs-deepnlp-2017/lectures
• [ ] https://github.com/rhnvrm/universe-coaster-racer-challenge
• [ ] https://github.com/random-forests/tutorials
• [ ] Machine Learning Recipes with Josh Gordon
• [ ] Reinforcement Learning: An Introduction
• [ ] https://github.com/eriklindernoren/ML-From-Scratch
• [ ] https://github.com/stanfordnlp/cs224n-winter17-notes
• [ ] https://medium.com/@ageitgey/machine-learning-is-fun-part-4-modern-face-recognition-with-deep-learning-c3cffc121d78#.7tyqpjz6q

ML:

• [ ] 【从零单排——数学白痴也能玩机器学习】
• [ ] 深度学习入门必看的书和论文？有哪些必备的技能需学习？
• [ ] 机器视觉、图像处理、机器学习领域相关代码和工程项目和数据集 集合
• [ ] CNN(卷积神经网络)、RNN(循环神经网络)、DNN(深度神经网络)的内部网络结构有什么区别？
• [ ] 简单解释一下sparse autoencoder, sparse coding和restricted boltzmann machine的关系？
• [ ] 在 Deep Learning / Machine Learning 领域，C++ 应如何学习？
• [ ] C++ neural network library
• [ ] theano
• [ ] TensorFlow
• [ ] torch7
• [ ] EXPLAINING AND HARNESSING ADVERSARIAL EXAMPLES and The Flaw Lurking In Every Deep Neural Net
• [ ] https://github.com/rasbt/python-machine-learning-book
• [ ] https://github.com/terryum/awesome-deep-learning-papers
• [ ] https://github.com/sjchoi86/dl_tutorials
• [ ] http://mp.weixin.qq.com/s?__biz=MzA3MTU0MzcyMQ==&mid=2447602671&idx=1&sn=700ffa7c1a01daa9b5550cc173609925&scene=1&
• [ ] https://zhuanlan.zhihu.com/p/22308032
• [ ] https://zhuanlan.zhihu.com/p/22107715
• [ ] https://www.quora.com/What-are-the-best-resources-to-learn-about-deep-learning

NLP:

• [ ] A Convolutional Neural Network for Modelling Sentences
• [ ] 基于深度学习的自然语言处理在2016年有哪些值得期待的发展？
• [ ] 一个玩得停不下来的Google神器：Ngram
• [ ] 如何评价SyntaxNet？

CV:

• [ ] 学习了哪些知识，计算机视觉才算入门？
• [ ] CS231n课程笔记翻译：图像分类笔记
• [ ] 学习SLAM需要哪些预备知识？

CG:

• [ ] Vulkan - 高性能渲染

Others:

• [ ] https://github.com/vic317yeh/One-Click-to-Be-Pro
• [ ] https://github.com/geekan/one-python
• [ ] https://zhuanlan.zhihu.com/p/22308870
• [ ] https://zhuanlan.zhihu.com/p/20092285
• [ ] https://zhuanlan.zhihu.com/p/22126107

[utensil]

Nupic:

• [ ] Search for the right paper about HTM
• [ ] Spatial Pooler Algorithm Implementation and Pseudocode
• [ ] Temporal Memory Algorithm Implementation and Pseudocode
• [ ] Continuous online sequence learning with an unsupervised neural network model
• [ ] Understanding LSTM Networks
• [ ] HTM.Julia
• [ ] Bare-Bone HTM
• [ ] HTM CLA Flow Chart Diagrams

[utensil]

QM:

• [ ] Single-world interpretations of quantum theory cannot be self-consistent

[utensil]

Deep Learning - The Straight Dope:

http://gluon.mxnet.io/

https://zhuanlan.zhihu.com/p/28648399

[utensil]

• https://github.com/karpathy/convnetjs

• http://cs.stanford.edu/people/karpathy/convnetjs/demo/classify2d.html

• http://www.cs.ubc.ca/~van/papers/2016-TOG-deepRL/index.html

• https://github.com/janesjanes/sketchy

• http://scs.ryerson.ca/~aharley/vis/conv/

• http://cs.stanford.edu/people/karpathy/convnetjs/demo/cifar10.html

• https://deepart.io/

• https://www.youtube.com/playlist?list=PLiaHhY2iBX9hdHaRr6b7XevZtgZRa1PoU

• https://www.youtube.com/playlist?list=PLE6Wd9FR--EfW8dtjAuPoTuPcqmOV53Fu

• http://swanintelligence.com/first-steps-with-neural-nets-in-keras.html

• http://neuralnetworksanddeeplearning.com/

• http://www.deeplearningbook.org/

• http://boxcar2d.com/index.html

• https://www.flickr.com/photos/syntopia/6791724773/in/photostream/

• http://blog.hvidtfeldts.net/index.php/2012/01/knots-and-polyhedra/

• http://blog.mathteachersresource.com/?p=670

• http://www.gitxiv.com/

• http://colah.github.io/posts/2014-03-NN-Manifolds-Topology/

• https://github.com/colah/Visualizing-Deep-Learning

• https://distill.pub/2017/feature-visualization/

• https://github.com/ChristosChristofidis/awesome-deep-learning#videos-and-lectures

• https://github.com/guillaume-chevalier/Awesome-Deep-Learning-Resources#youtube

• https://github.com/kjw0612/awesome-deep-vision#videos

[utensil]

Datasets:

• All
  • https://github.com/caesar0301/awesome-public-datasets
  • http://academictorrents.com/
• CV
  • http://deeplearning.net/datasets
• Go
  • https://github.com/yenw/computer-go-dataset
• NLP
  • http://universaldependencies.org/
  • https://github.com/Breakend/DialogDatasets

[utensil]

Notebooks:

• http://scikit-learn.org/dev/_static/ml_map.png
• https://github.com/donnemartin/data-science-ipython-notebooks
• https://github.com/jakevdp/PythonDataScienceHandbook

Foundation:

• https://github.com/rushter/MLAlgorithms/
• https://github.com/eriklindernoren/ML-From-Scratch

[utensil]

https://en.wikipedia.org/wiki/Arg_max https://tex.stackexchange.com/questions/5223/command-for-argmin-or-argmax https://en.wikibooks.org/wiki/LaTeX/Advanced_Mathematics https://www.cs.ubc.ca/~schmidtm/Documents/2016_540_Argmax.pdf https://raw.githubusercontent.com/scikit-learn/scikit-learn/master/doc/modules/linear_model.rst