Intro

• http://www.wildml.com/2015/09/implementing-a-neural-network-from-scratch/
• http://iamtrask.github.io/2015/07/12/basic-python-network/
• https://iamtrask.github.io/2015/07/27/python-network-part2/
• https://www.analyticsvidhya.com/blog/2016/08/deep-learning-path/
• http://neuralnetworksanddeeplearning.com/index.html
• https://github.com/adeshpande3/NLP-Stuff
• https://adeshpande3.github.io/adeshpande3.github.io/Deep-Learning-Research-Review-Week-3-Natural-Language-Processing
• http://karpathy.github.io/neuralnets/ https://github.com/terryum/awesome-deep-learning-papers http://deeplearning.stanford.edu/wiki/index.php/UFLDL_Tutorial

Probabilities and Statistics

http://www.dartmouth.edu/~chance/teaching_aids/books_articles/probability_book/book.html

http://static1.squarespace.com/static/54bf3241e4b0f0d81bf7ff36/t/55e9494fe4b011aed10e48e5/1441352015658/probability_cheatsheet.pdf

http://cs229.stanford.edu/section/cs229-prob.pdf

https://github.com/rouseguy/intro2stats

http://stattrek.com/tutorials/statistics-tutorial.aspx

• Calculus https://www.coursera.org/learn/calculus1
• https://www.youtube.com/embed/54_XRjHhZzI?feature=oembed
• http://www-math.mit.edu/~djk/calculus_beginners/

• https://www.math.hmc.edu/calculus/tutorials/

• Python https://www.coursera.org/learn/python
• https://www.coursera.org/specializations/python
• http://www.tutorialspoint.com/python/
• http://www.learnpython.org/

Linear Algebra

http://stattrek.com/tutorials/matrix-algebra-tutorial.aspx

http://www.deeplearningbook.org/slides/02_linear_algebra.pdf

http://cs229.stanford.edu/section/cs229-linalg.pdf

https://www.khanacademy.org/math/linear-algebra

http://ocw.mit.edu/courses/mathematics/18-06sc-linear-algebra-fall-2011/

https://www.math.ucdavis.edu/~linear/linear.pdf

Dimensionality Reduction

http://glowingpython.blogspot.com/2011/06/svd-decomposition-with-numpy.html

http://radialmind.blogspot.com/2009/11/svd-in-python.html

http://bigdata-madesimple.com/decoding-dimensionality-reduction-pca-and-svd/

http://blog.josephwilk.net/projects/latent-semantic-analysis-in-python.html

http://bl.ocks.org/ktaneishi/9499896#pca.js

http://www.cs.cmu.edu/~christos/TALKS/09-KDD-tutorial

http://glowingpython.blogspot.com/2011/05/latent-semantic-analysis-with-term.html

http://glowingpython.blogspot.com/2011/07/principal-component-analysis-with-numpy.html

http://glowingpython.blogspot.com/2011/09/eigenvectors-animated-gif.html

http://www.denizyuret.com/2005/08/singular-value-decomposition-notes.html

http://www.kdnuggets.com/2016/06/nutrition-principal-component-analysis-tutorial.html

http://cs.stanford.edu/people/karpathy/tsnejs/

Logistic Regression

https://triangleinequality.wordpress.com/2013/12/02/logistic-regression/

http://www.dataschool.io/logistic-regression-in-python-using-scikit-learn/

http://deeplearning.net/software/theano/tutorial/examples.html#a-real-example-logistic-regression

http://deeplearning.net/tutorial/logreg.html

https://florianhartl.com/logistic-regression-geometric-intuition.html

sk-learn

http://peekaboo-vision.blogspot.cz/2013/01/machine-learning-cheat-sheet-for-scikit.html

https://github.com/aigamedev/scikit-neuralnetwork

http://www.kdnuggets.com/2016/01/scikit-learn-tutorials-introduction-classifiers.html

https://github.com/mmmayo13/scikit-learn-classifiers

https://pythonprogramming.net/flat-clustering-machine-learning-python-scikit-learn/

https://www.analyticsvidhya.com/blog/2016/08/tutorial-data-science-command-line-scikit-learn/

https://www.analyticsvidhya.com/blog/2016/07/practical-guide-data-preprocessing-python-scikit-learn/

http://www.markhneedham.com/blog/2015/02/15/pythonscikit-learn-calculating-tfidf-on-how-i-met-your-mother-transcripts/

https://github.com/GaelVaroquaux/scikit-learn-tutorial

https://github.com/justmarkham/scikit-learn-videos

https://pythonprogramming.net/machine-learning-python-sklearn-intro/

Theano

http://nbviewer.jupyter.org/github/craffel/theano-tutorial/blob/master/Theano%20Tutorial.ipynb

https://github.com/goodfeli/theano_exercises

http://deeplearning.net/tutorial/

http://deeplearning.net/reading-list

http://deeplearning.net/tutorial/dA.html

http://deeplearning.net/tutorial/deeplearning.pdf - Just tutorials from the source above

http://deeplearning.net/software/theano/ - Scientific computing framework in Python

https://pypi.python.org/pypi/theanets

http://deeplearning.net/software/theano/tutorial/gradients.html

http://deeplearning.net/tutorial/logreg.html#logreg

http://deeplearning.net/software/theano/tutorial/

https://github.com/goodfeli/theano_exercises

https://github.com/Newmu/Theano-Tutorials

https://www.analyticsvidhya.com/blog/2016/04/neural-networks-python-theano/

http://outlace.com/Beginner-Tutorial-Theano/

http://www.marekrei.com/blog/theano-tutorial/

Keras

https://github.com/fchollet/keras - Extension of Theano, meant specifically for ANN work

https://keras.io/

https://blog.keras.io/introducing-keras-10.html

https://blog.keras.io/keras-as-a-simplified-interface-to-tensorflow-tutorial.html

https://github.com/fchollet/keras/blob/master/examples/imdb_lstm.py

http://stackoverflow.com/questions/43457890/multiprocessing-with-gpu-in-keras

https://www.analyticsvidhya.com/blog/2016/10/tutorial-optimizing-neural-networks-using-keras-with-image-recognition-case-study/

https://blog.keras.io/running-jupyter-notebooks-on-gpu-on-aws-a-starter-guide.html

https://blog.keras.io/building-autoencoders-in-keras.html

https://blog.keras.io/how-convolutional-neural-networks-see-the-world.html

http://machinelearningmastery.com/tutorial-first-neural-network-python-keras/

https://www.datacamp.com/community/blog/keras-cheat-sheet#gs.E5Tf5x8

https://github.com/fchollet/keras-resources

Perceptrons

• https://datasciencelab.wordpress.com/2014/01/10/machine-learning-classics-the-perceptron/
• https://triangleinequality.wordpress.com/2014/02/24/enter-the-perceptron/
• http://glowingpython.blogspot.com/2011/10/perceptron.html

word2vec/embeddings

• http://radimrehurek.com/gensim/models/word2vec.html - Gensim implementation of Word2Vec
• https://radimrehurek.com/gensim/tut1.html
• https://radimrehurek.com/gensim/tutorial.html
• https://code.google.com/p/word2vec/ - Google implementation of word2vec
• http://alexminnaar.com/word2vec-tutorial-part-i-the-skip-gram-model.html - Word2Vec
• http://rare-technologies.com/word2vec-tutorial/ - Gensim Word2Vec tutorial (training, loading, using, etc.)
• https://rare-technologies.com/making-sense-of-word2vec/
• https://rare-technologies.com/fasttext-and-gensim-word-embeddings/
• https://research.facebook.com/blog/fasttext/
• https://www.kaggle.com/c/word2vec-nlp-tutorial
• http://www-personal.umich.edu/~ronxin/pdf/w2vexp.pdf - Detailed write-up explaining Word2Vec
• https://code.google.com/p/word2vec/
• https://code.google.com/p/word2vec/source/browse/trunk/
• http://u.cs.biu.ac.il/~nlp/resources/downloads/word2parvec/
• https://deeplearning4j.org/word2vec.html
• http://textminingonline.com/getting-started-with-word2vec-and-glove-in-python
• http://www.johnwittenauer.net/language-exploration-using-vector-space-models/
• https://radimrehurek.com/gensim/models/doc2vec.html

LSTM

• https://iamtrask.github.io/2015/11/15/anyone-can-code-lstm/
• http://colah.github.io/posts/2015-08-Understanding-LSTMs/
• http://www.cs.toronto.edu/~graves/handwriting.html
• https://en.wikipedia.org/wiki/Long_short-term_memory - Wikipedia article about LSTMs
• https://github.com/HendrikStrobelt/lstmvis

https://github.com/wojzaremba/lstm

http://lstm.seas.harvard.edu/

https://github.com/stanfordnlp/treelstm

https://github.com/microth/PathLSTM

https://github.com/XingxingZhang/td-treelstm

http://deeplearning.net/tutorial/lstm.html#lstm

https://apaszke.github.io/lstm-explained.html

https://deeplearning4j.org/lstm.html

https://github.com/dennybritz/rnn-tutorial-gru-lstm

http://deeplearning.net/tutorial/lstm.html#lstm

Embeddings

http://ronxin.github.io/wevi/

https://github.com/ronxin/wevi wevi (from Rong Xin)

https://levyomer.wordpress.com/2014/04/25/dependency-based-word-embeddings/ Dependency-based word embeddings

https://github.com/stanfordnlp/GloVe

http://nlp.stanford.edu/projects/glove

https://github.com/maciejkula/glove-python

http://lebret.ch/words/word embeddings from Remi Lebret (+ a tool for generating embeddings)

http://metaoptimize.com/projects/wordreprs/ embeddings and tools for basic NLP tasks

http://wordvectors.org/suite.php

word similarity data sets

http://wordvectors.org/suite.php

http://deeplearning4j.org/eigenvector

http://wordvectors.org/

http://metaoptimize.com/projects/wordreprs/

https://github.com/semanticvectors/semanticvectors/wiki

http://clic.cimec.unitn.it/composes/semantic-vectors.html

https://blog.acolyer.org/2016/04/21/the-amazing-power-of-word-vectors/

https://www.kaggle.com/c/word2vec-nlp-tutorial/details/part-1-for-beginners-bag-of-words

https://www.kaggle.com/c/word2vec-nlp-tutorial/details/part-2-word-vectors

https://www.kaggle.com/c/word2vec-nlp-tutorial/details/part-3-more-fun-with-word-vectors

https://www.kaggle.com/c/word2vec-nlp-tutorial/details/part-4-comparing-deep-and-non-deep-learning-methods

http://ronan.collobert.com/senna/

http://ml.nec-labs.com/senna/ Code and embeddings from SENNA.

http://colinmorris.github.io/blog/1b-words-char-embeddings

http://www.cis.upenn.edu/~ungar/eigenwords/

http://www.offconvex.org/2016/07/10/embeddingspolysemy/

http://www.tensorflow.org/tutorials/word2vec/index.md

https://www.tensorflow.org/versions/r0.11/tutorials/word2vec/index.html

http://ronxin.github.io/lamvi/dist/#model=word2vec&backend=browser&query_in=good&query_out=G_bennet,B_circumstances

https://www.quora.com/How-does-word2vec-work/answer/Ajit-Rajasekharan

http://mccormickml.com/2016/04/12/googles-pretrained-word2vec-model-in-python/

Autoencoders

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

http://ufldl.stanford.edu/tutorial/unsupervised/Autoencoders/

https://triangleinequality.wordpress.com/2014/08/12/theano-autoencoders-and-mnist/

Introductions

• http://www.kdnuggets.com/2016/10/beginners-guide-neural-networks-python-scikit-learn.html
• http://cl.naist.jp/~kevinduh/a/deep2014/ - Kevin Duh lectures
• http://www.deeplearningbook.org/ Deep Learning Book
• http://ciml.info/ - Hal Daume’s book
• http://nlp.stanford.edu/courses/NAACL2013/ Deep Learning for NLP Without Magic
• http://info.usherbrooke.ca/hlarochelle/neural_networks/content.html
• http://www.deeplearning.net/

Tutorials, software packages, datasets, and readings (in Theano)

http://web.stanford.edu/~jurafsky/slp3/

Jurafsky - chapter 19 (?) about word2vec and related methods

http://u.cs.biu.ac.il/~yogo/nnlp.pdf

Yoav Goldberg - Primer on Neural Network Models for NLPhttp://neuralnetworksanddeeplearning.com/

http://neuralnetworksanddeeplearning.com/chap1.html

http://neuralnetworksanddeeplearning.com/chap2.html

http://neuralnetworksanddeeplearning.com/chap3.html

http://neuralnetworksanddeeplearning.com/chap4.html

http://neuralnetworksanddeeplearning.com/chap5.html

http://neuralnetworksanddeeplearning.com/chap6.html

https://github.com/neubig/nlptutorial

http://deeplearning.net/reading-list/

• Summarization https://github.com/gregdurrett/berkeley-doc-summarizer
• http://nlp.cs.berkeley.edu/projects/summarizer.shtml

https://www.linkedin.com/pulse/lex-rank-textrank-based-document-summarization-system-niraj-kumar

https://research.googleblog.com/2016/08/text-summarization-with-tensorflow.html?m=1

http://rare-technologies.com/text-summarization-with-gensim/

https://rare-technologies.com/text-summarization-in-python-extractive-vs-abstractive-techniques-revisited/

https://github.com/tensorflow/models/tree/master/textsum

https://github.com/harvardnlp/NAMAS

https://github.com/carpedm20/neural-summary-tensorflow

• Neural Machine Translation http://lisa.iro.umontreal.ca/mt-demo
• https://github.com/mila-udem/blocks-examples/tree/master/machine_translation
• https://github.com/nyu-dl/dl4mt-tutorial - dl4mt
• https://github.com/lmthang/nmt.matlab
• https://github.com/neubig/nmt-tips
• https://github.com/jonsafari/nmt-list
• https://research.googleblog.com/2016/09/a-neural-network-for-machine.html
• https://devblogs.nvidia.com/parallelforall/introduction-neural-machine-translation-with-gpus/
• https://devblogs.nvidia.com/parallelforall/introduction-neural-machine-translation-gpus-part-2/
• https://devblogs.nvidia.com/parallelforall/introduction-neural-machine-translation-gpus-part-3/
• https://research.googleblog.com/2016/11/zero-shot-translation-with-googles.html
• https://sites.google.com/site/acl16nmt/

Natural Language Generation

https://github.com/simplenlg

https://github.com/nltk/nltk_contrib/tree/master/nltk_contrib/fuf

https://aclweb.org/aclwiki/index.php?title=Downloadable_NLG_systems

Question Answering

http://www.kdnuggets.com/2015/11/deep-learning-visual-question-answering.html

https://github.com/jcoreyes/NLQA

https://github.com/jcoreyes/NLQA/tree/master/qanta

https://rajpurkar.github.io/SQuAD-explorer/

https://github.com/fh295/DefGen2

http://www.visualqa.org/

http://cs.nyu.edu/~kcho/DMQA/

NLP General

http://blog.mashape.com/list-of-25-natural-language-processing-apis/ 25 NLP APIs

http://www.denizyuret.com/2015/07/parsing-with-word-vectors.html

http://www.denizyuret.com/2015/03/parallelizing-parser.html

http://memkite.com/deep-learning-bibliography/#natural_language_processing

http://www.kdnuggets.com/2015/12/natural-language-processing-101.html

https://techcrunch.com/2016/07/20/google-launches-new-api-to-help-you-parse-natural-language/

http://www.degeneratestate.org/posts/2016/Apr/20/heavy-metal-and-natural-language-processing-part-1/

http://www.degeneratestate.org/posts/2016/Sep/12/heavy-metal-and-natural-language-processing-part-2/

http://metamind.io/research/multiple-different-natural-language-processing-tasks-in-a-single-deep-model/

https://gigadom.wordpress.com/2015/10/02/natural-language-processing-what-would-shakespeare-say/

https://blog.monkeylearn.com/the-definitive-guide-to-natural-language-processing/

NLTK

http://www.nltk.org/book/ NLTK Book

https://pythonprogramming.net/tokenizing-words-sentences-nltk-tutorial/

https://www.youtube.com/watch?v=FLZvOKSCkxY&list=PLQVvvaa0QuDf2JswnfiGkliBInZnIC4HL

http://textminingonline.com/dive-into-nltk-part-i-getting-started-with-nltk

Tokenizing words and sentenceshttp://glowingpython.blogspot.com/2013/07/combining-scikit-learn-and-ntlk.html

Image Processing

https://pythonprogramming.net/image-recognition-python/

Support Vector Machines

https://pythonprogramming.net/linear-svc-example-scikit-learn-svm-python/

http://tullo.ch/articles/svm-py/

https://github.com/ajtulloch/svmpy

https://www.quora.com/What-does-support-vector-machine-SVM-mean-in-laymans-terms

https://www.quora.com/How-does-deep-learning-work-and-how-is-it-different-from-normal-neural-networks-and-or-SVM

https://github.com/mesnilgr/nbsvm

https://www.csie.ntu.edu.tw/%7Ecjlin/libsvm/

• Conditional Random Fields http://sourceforge.net/projects/crfpp/files/crfpp/0.54/
• http://blog.echen.me/2012/01/03/introduction-to-conditional-random-fields/

Convolutional NN

• http://www.wildml.com/2015/11/understanding-convolutional-neural-networks-for-nlp/
• http://stats.stackexchange.com/questions/114385/what-is-the-difference-between-convolutional-neural-networks-restricted-boltzma
• http://www.wildml.com/2015/12/implementing-a-cnn-for-text-classification-in-tensorflow/
• http://www.kdnuggets.com/2015/11/understanding-convolutional-neural-networks-nlp.html
• http://cs.stanford.edu/people/karpathy/convnetjs/
• http://colah.github.io/posts/2014-07-Understanding-Convolutions/
• http://colah.github.io/posts/2014-07-Conv-Nets-Modular/
• http://cs231n.github.io/convolutional-networks/
• http://www.kdnuggets.com/2016/06/peeking-inside-convolutional-neural-networks.html
• http://www.kdnuggets.com/2015/11/understanding-convolutional-neural-networks-nlp.html
• http://www.kdnuggets.com/2015/04/inside-deep-learning-computer-vision-convolutional-neural-networks.html
• http://www.kdnuggets.com/2016/09/beginners-guide-understanding-convolutional-neural-networks-part-1.html
• http://www.kdnuggets.com/2016/09/beginners-guide-understanding-convolutional-neural-networks-part-2.html
• http://brohrer.github.io/how_convolutional_neural_networks_work.html https://github.com/hohoCode/textSimilarityConvNet
• https://www.analyticsvidhya.com/blog/2016/04/deep-learning-computer-vision-introduction-convolution-neural-networks/
• http://www.kdnuggets.com/2016/11/intuitive-explanation-convolutional-neural-networks.html
• https://github.com/dennybritz/cnn-text-classification-tf
• http://scs.ryerson.ca/~aharley/vis/conv/
• https://ujjwalkarn.me/2016/08/11/intuitive-explanation-convnets/
• https://github.com/yoonkim/CNN_sentence
• https://adeshpande3.github.io/adeshpande3.github.io/A-Beginner’s-Guide-To-Understanding-Convolutional-Neural-Networks/
• https://adeshpande3.github.io/adeshpande3.github.io/A-Beginner’s-Guide-To-Understanding- Convolutional-Neural-Networks-Part-2/
• http://homepages.inf.ed.ac.uk/mlap/resources/cnnhlights/
• https://algobeans.com/2016/01/26/introduction-to-convolutional-neural-network/

Recurrent NN

• http://www.wildml.com/2015/09/recurrent-neural-networks-tutorial-part-1-introduction-to-rnns/
• http://www.wildml.com/2015/09/recurrent-neural-networks-tutorial-part-2-implementing-a-language-model-rnn-with-python-numpy-and-theano/
• http://www.wildml.com/2015/10/recurrent-neural-networks-tutorial-part-3-backpropagation-through-time-and-vanishing-gradients/
• http://www.wildml.com/2015/10/recurrent-neural-network-tutorial-part-4-implementing-a-grulstm-rnn-with-python-and-theano/
• http://www.kdnuggets.com/2015/12/deep-learning-outgrows-bag-words-recurrent-neural-networks.html
• http://www.kdnuggets.com/2015/06/rnn-tutorial-sequence-learning-recurrent-neural-networks.html
• http://www.kdnuggets.com/2015/10/recurrent-neural-networks-tutorial.html
• http://karpathy.github.io/2015/05/21/rnn-effectiveness/
• http://colah.github.io/posts/2014-07-NLP-RNNs-Representations/
• https://github.com/karpathy/char-rnn
• http://www.kdnuggets.com/2016/05/intro-recurrent-networks-tensorflow.html
• http://www.kdnuggets.com/2015/10/recurrent-neural-networks-tutorial.html
• http://www.kdnuggets.com/2015/06/rnn-tutorial-sequence-learning-recurrent-neural-networks.html
• http://www.kdnuggets.com/2015/11/samim-recurrent-neural-net-describe-images-taylor-swift.html
• http://research.microsoft.com/en-us/projects/rnn/

http://www.rnnlm.org/

http://distill.pub/2016/augmented-rnns/

https://github.com/distillpub/post–augmented-rnns

https://github.com/dennybritz/tf-rnn

https://github.com/dennybritz/rnn-tutorial-rnnlm

http://www.wildml.com/2016/08/rnns-in-tensorflow-a-practical-guide-and-undocumented-features/

https://github.com/shawnwun/RNNLG

https://github.com/isi-nlp/Zoph_RNN

https://github.com/facebook/Stack-RNN

https://github.com/kjw0612/awesome-rnn

Sequence to sequence

• http://www.tensorflow.org/tutorials/seq2seq/index.md https://github.com/harvardnlp/seq2seq-attn

• https://www.tensorflow.org/versions/r0.12/tutorials/seq2seq/index.html#sequence-to-sequence-models
• https://github.com/farizrahman4u/seq2seq

k-means

https://datasciencelab.wordpress.com/2013/12/12/clustering-with-k-means-in-python/

https://datasciencelab.wordpress.com/2014/01/21/selection-of-k-in-k-means-clustering-reloaded/

http://glowingpython.blogspot.com/2012/04/k-means-clustering-with-scipy.html

https://codesachin.wordpress.com/2015/11/14/k-means-clustering-with-tensorflow/

http://stanford.edu/class/ee103/visualizations/kmeans/kmeans.html

k-nearest neighbors

http://glowingpython.blogspot.com/2012/04/k-nearest-neighbour-classifier.html

http://glowingpython.blogspot.com/2012/04/k-nearest-neighbor-search.html

Recursive NN

• http://www.kdnuggets.com/2016/06/recursive-neural-networks-tensorflow.html

• https://pseudoprofound.wordpress.com/2016/06/20/recursive-not-recurrent-neural-nets-in-tensorflow/

• Network Analysishttp://snap.stanford.edu/node2vec/

http://glowingpython.blogspot.com/2012/11/first-steps-with-networx.html

http://glowingpython.blogspot.com/2013/02/betweenness-centrality.html

https://snap.stanford.edu/data/

https://pypi.python.org/pypi/python-graph

http://glowingpython.blogspot.com/2011/05/four-ways-to-compute-google-pagerank.html

https://www.quora.com/Is-there-a-simple-explanation-of-the-Louvain-Method-of-community-detection

• Tagging and Parsing https://spacy.io/blog/parsing-english-in-python
• Parsing English in Python https://github.com/clir/clearnlp
• https://pypi.python.org/pypi/bllipparser/
• https://github.com/BLLIP/bllip-parser
• http://nlp.stanford.edu/software/lex-parser.shtml
• http://nlp.stanford.edu/software/tagger.shtml
• https://code.google.com/p/universal-pos-tags/
• http://www.ark.cs.cmu.edu/TweetNLP/
• https://code.google.com/p/berkeleyparser
• http://www.cs.columbia.edu/~mcollins/code.html
• http://www.ark.cs.cmu.edu/TurboParser/
• http://demo.ark.cs.cmu.edu/parse
• https://github.com/tensorflow/models/tree/master/syntaxnet/syntaxnet/models/parsey_mcparseface
• https://github.com/tensorflow/models/tree/master/syntaxnet https://github.com/tensorflow/models/blob/master/syntaxnet/universal.md
• https://research.googleblog.com/2016/05/announcing-syntaxnet-worlds-most.html
• https://research.googleblog.com/2011/03/building-resources-to-syntactically.html
• https://research.googleblog.com/2016/08/meet-parseys-cousins-syntax-for-40.html http://universaldependencies.org/

• https://github.com/tensorflow/models/tree/master/syntaxnet

• Semantic Parsing https://github.com/wcmac/sippycup Assignment from Stanford

http://nbviewer.jupyter.org/github/wcmac/sippycup/blob/master/sippycup-unit-0.ipynb

http://nbviewer.ipython.org/github/wcmac/sippycup/blob/master/sippycup-unit-1.ipynb

http://nbviewer.ipython.org/github/wcmac/sippycup/blob/master/sippycup-unit-2.ipynb

http://nbviewer.ipython.org/github/wcmac/sippycup/blob/master/sippycup-unit-3.ipynb

http://nbviewer.jupyter.org/github/cgpotts/cs224u/blob/master/semparse_homework.ipynb

http://www.ark.cs.cmu.edu/SEMAFOR/

http://amr.isi.edu/research.html

https://github.com/c-amr/camr

http://www.isi.edu/natural-language/software/amrparser.tar.gz

http://www.isi.edu/natural-language/software/amr2eng.zip

http://www.dipanjandas.com/files/reddy.etal.2016.pdf

Transforming Dependency Structures to Logical Forms for Semantic Parsing https://github.com/sivareddyg/deplambda

http://www-nlp.stanford.edu/software/sempre/

https://github.com/percyliang/sempre

http://nlp.stanford.edu/projects/snli/

The Stanford Natural Language Inference (SNLI) Corpus

CCG

https://github.com/mikelewis0/easyccg

http://openccg.sourceforge.net/

https://github.com/OpenCCG/openccg

http://openccg.sourceforge.net/

Linear Regression

https://triangleinequality.wordpress.com/2013/11/17/linear-regression-the-maths/

https://triangleinequality.wordpress.com/2013/11/28/linear-regression-the-code/

http://glowingpython.blogspot.com/2012/03/linear-regression-with-numpy.html

http://www.kdnuggets.com/2016/06/brief-primer-linear-regression-part-1.html

http://www.kdnuggets.com/2016/06/brief-primer-linear-regression-part-2.html

https://github.com/justmarkham/DAT4/blob/master/notebooks/08_linear_regression.ipynb

http://www.kdnuggets.com/2016/11/linear-regression-least-squares-matrix-multiplication-concise-technical-overview.html

numpy

http://glowingpython.blogspot.com/2012/01/monte-carlo-estimate-for-pi-with-numpy.html

Neural Attention Models

• http://www.kdnuggets.com/2016/01/attention-memory-deep-learning-nlp.html
• https://github.com/facebook/NAMAS
• http://www.wildml.com/2016/01/attention-and-memory-in-deep-learning-and-nlp/
• http://groups.inf.ed.ac.uk/cup/codeattention/
• https://www.opendatascience.com/blog/attention-and-memory-in-deep-learning-and-nlp/

Topic Modeling

https://algobeans.com/2015/06/21/laymans-explanation-of-topic-modeling-with-lda-2/

https://www.analyticsvidhya.com/blog/2016/08/beginners-guide-to-topic-modeling-in-python/

http://www.cs.columbia.edu/~blei/topicmodeling_software.html

http://blog.echen.me/2011/08/22/introduction-to-latent-dirichlet-allocation/

Dialogue Systems

http://www.wildml.com/2016/04/deep-learning-for-chatbots-part-1-introduction/

http://www.wildml.com/2016/07/deep-learning-for-chatbots-2-retrieval-based-model-tensorflow/

• Videos of presentations https://www.youtube.com/watch?v=qSA9v7ZkC7Q&feature=youtu.be

Lecture by Chris Potts on Distributed word representations: dimensionality reduction https://www.youtube.com/watch?v=JSNZA8jVcm4

Schmidhuber https://www.youtube.com/watch?v=HrMU1GgyxL8 LeCun

https://www.youtube.com/watch?v=DLItuVVKJOw Duh (part 1 of 4)

• Skip-thoughts https://github.com/ryankiros/skip-thoughts
• https://github.com/kyunghyuncho/skip-thoughts
• https://gab41.lab41.org/lab41-reading-group-skip-thought-vectors-fec68c05aa92
• http://www.kdnuggets.com/2016/11/deep-learning-group-skip-thought-vectors.html
• http://deeplearning4j.org/thoughtvectors
• http://gabgoh.github.io/ThoughtVectors/

Sentiment

http://sentiment.christopherpotts.net/ - Tutorial on deep sentiment analysis

http://sentiment.christopherpotts.net/lexicons.html

http://nlp.stanford.edu/sentiment/- dataset (and code) for Richard Socher’s sentiment system

http://www.kdnuggets.com/2015/12/sentiment-analysis-101.html

http://sentiment140.com

Bibliographies

http://clair.si.umich.edu/homepage/bib2html/dl.pdf Deep Learning and NLP bib (made by UMich)

http://clair.si.umich.edu/homepage/bib2html/dl.bib bibtex file for the above PDF

http://clair.si.umich.edu/clair/homepage/bib2html/misc-bib.html Misc. bib (compiled by UMich)

• Courses http://cs224d.stanford.edu/syllabus.html Deep Learning for NLP @ Stanford

http://ace.cs.ohiou.edu/~razvan/courses/dl6890/index.html

https://www.youtube.com/playlist?list=PL6Xpj9I5qXYEcOhn7TqghAJ6NAPrNmUBH Neural networks class - Universitй de Sherbrooke

http://web.stanford.edu/class/cs224w/

Social and Information Network Analysis - Jure Leskovec http://rll.berkeley.edu/deeprlcourse/

Deep RL at Berkeley https://github.com/thejakeyboy/umich-eecs545-lectures

Jake Abernethy’s 545 at Michigan https://github.com/lmarti/machine-learning

https://classroom.udacity.com/courses/ud730

Vincent Vanhoucke

https://ocw.mit.edu/courses/electrical-engineering-and-computer-science/6-034-artificial-intelligence-fall-2010/lecture-videos/

Winson @MIT (AI) https://www.youtube.com/playlist?list=PLehuLRPyt1Hyi78UOkMPWCGRxGcA9NVOE STAT 946: Deep Learning, Ali Ghodsi

https://www.college-de-france.fr/site/en-yann-lecun/course-2015-2016.htm

https://people.duke.edu/~ccc14/sta-663/

https://web.stanford.edu/class/cs234/

http://web.stanford.edu/class/cs224u

http://web.stanford.edu/class/cs224d/

http://www.holehouse.org/mlclass/

http://web.stanford.edu/class/cs20si/index.html

http://web.stanford.edu/class/cs331b/

http://ttic.uchicago.edu/~dmcallester/DeepClass/class.html

Quora links

https://www.quora.com/What-are-the-best-resources-to-learn-about-deep-learning

https://www.quora.com/What-are-some-good-resources-to-learn-about-deep-learning-in-Natural- Language-Processing

• Tutorials http://icml.cc/2015/tutorials/icml2015-nlu-tutorial.pdf Percy Liang Tutorial

• Backpropagation http://colah.github.io/posts/2015-08-Backprop/

http://code.activestate.com/recipes/578148-simple-back-propagation-neural-network-in-python-s/

Visualization

http://colah.github.io/posts/2014-10-Visualizing-MNIST/

http://colah.github.io/posts/2014-07-FFN-Graphs-Vis/

http://www.kdnuggets.com/2015/11/overview-python-visualization-tools.html

http://glowingpython.blogspot.com/2012/10/visualizing-correlation-matrices.html

http://bl.ocks.org/ktaneishi/9265946

dendrogramhttp://www.graphviz.org/Download.php

• Python https://github.com/thejakeyboy/Python-Lectures

• Language Modeling https://github.com/turian/neural-language-model
• http://www.foldl.me/2014/kneser-ney-smoothing/

http://beyondexpectations.quora.com/An-Intuitive-Explanation-of-Good-Turing-Smoothing

https://github.com/turian/neural-language-model - Code for various neural language models

http://statmt.org/ngrams/

TensorFlow (731)

http://www.kdnuggets.com/2016/01/deep-learning-spark-tensorflow.html

http://playground.tensorflow.org

http://www.kdnuggets.com/2015/11/google-tensorflow-deep-learning-disappoints.html

https://github.com/tensorflow/models

https://www.tensorflow.org/versions/r0.10/tutorials/image_recognition/index.html

http://blog.aylien.com/introduction-generative-adversarial-networks-code-tensorflow/

https://github.com/tensorflow/models/tree/master/lm_1b

https://www.analyticsvidhya.com/blog/2016/10/an-introduction-to-implementing-neural-networks-using-tensorflow/

https://github.com/nlintz/TensorFlow-Tutorials

https://github.com/aymericdamien/TensorFlow-Examples

https://github.com/tensorflow/skflow

https://github.com/jtoy/awesome-tensorflow

https://github.com/pkmital/tensorflow_tutorials

https://github.com/nlintz/TensorFlow-Tutorials

http://www.kdnuggets.com/2016/08/gentlest-introduction-tensorflow-part-1.html

http://www.kdnuggets.com/2016/08/gentlest-introduction-tensorflow-part-2.html

http://www.kdnuggets.com/2017/02/gentlest-introduction-tensorflow-part-3.html

Information Extraction (232)

http://knowitall.cs.washington.edu/paralex/ http://openie.allenai.org/

http://reverb.cs.washington.edu/

https://github.com/dmorr-google/relation-extraction-corpus

http://www.chokkan.org/software/crfsuite/

http://mallet.cs.umass.edu/

• Reinforcement Learninghttp://www.wildml.com/2016/10/learning-reinforcement-learning/

• Graph-based learning

https://blog.insightdatascience.com/graph-based-machine-learning-6e2bd8926a0

https://blog.insightdatascience.com/graph-based-machine-learning-part-2-f7096c801bec

https://research.googleblog.com/2016/10/graph-powered-machine-learning-at-google.html

https://bitbucket.org/taynaud/python-louvain

• Mega lists http://blog.christianperone.com/

https://github.com/ChristosChristofidis/awesome-deep-learning

https://github.com/gutfeeling/beginner_nlp

https://github.com/andrewt3000/dl4nlp

https://github.com/ujjwalkarn/Machine-Learning-Tutorials/blob/master/README.md

https://github.com/ujjwalkarn/DataSciencePython

https://github.com/bulutyazilim/awesome-datascience

https://github.com/owainlewis/awesome-artificial-intelligence/blob/master/README.md[

http://deeplearning.net/software_links/

https://github.com/edobashira/speech-language-processing

http://www.johnwittenauer.net/a-compendium-of-machine-learning-resources/

http://www.jeremydjacksonphd.com/category/deep-learning/

http://meta-guide.com/software-meta-guide/100-best-github-deep-learning

Vision and images

http://www.spokenedition.com/the-intercept/fearless-adversarial-journalism/

http://www.kdnuggets.com/2016/08/seven-steps-understanding-computer-vision.html

https://adeshpande3.github.io/adeshpande3.github.io/Analyzing-the-Papers-Behind-Facebook’s-Computer-Vision-Approach/

https://vision.ece.vt.edu/clipart/

http://kelvinxu.github.io/projects/capgen.html

https://github.com/kelvinxu/arctic-captions

https://github.com/handee/opencv-gettingstarted

Speech http://kaldi-asr.org/

https://github.com/claritylab/lucida

http://speechkitchen.org/home/experiments/

http://www.speech.cs.cmu.edu/SLM/toolkit.html

https://sourceforge.net/projects/kaldi/

https://github.com/dennybritz/nn-from-scratch

https://github.com/dennybritz/deeplearning-papernotes

https://github.com/lisa-lab/pylearn2

http://deeplearning.stanford.edu/tutorial/

https://github.com/nitishsrivastava/deepnet

http://glowingpython.blogspot.com/2012/05/manifold-learning-on-handwritten-digits.html

http://glowingpython.blogspot.com/2013/04/real-time-twitter-analysis.html

http://glowingpython.blogspot.com/2013/01/bloom-filter.html

http://glowingpython.blogspot.com/2013/01/box-muller-transformation.html

http://web.eecs.umich.edu/~radev/intronlp/

https://github.com/ogrisel/parallel_ml_tutorial/blob/master/rendered_notebooks/03%20-%20Basic%20principles%20of%20Machine%20Learning.ipynb

https://github.com/jupyter/jupyter/wiki/A-gallery-of-interesting-Jupyter-Notebooks

https://seat.massey.ac.nz/personal/s.r.marsland/MLBook.html

https://github.com/ocelma/python-recsys

http://torch.ch/ - Scientific computing framework in LuaJIT

http://caffe.berkeleyvision.org/ - Deep learning framework in Python and Matlab

http://deeplearning4j.org/ - Deep learning framework in Java

http://cs224d.stanford.edu/reports.html - Final reports from the Stanford DL for NLP class.

https://class.coursera.org/nlpintro-001/forum/thread?thread_id=104#post-410

http://www.kdnuggets.com/2015/01/deep-learning-explanation-what-how-why.html

http://www.kdnuggets.com/2015/12/machine-learning-data-science-apis.html

http://www.kdnuggets.com/2015/11/seven-steps-machine-learning-python.html

http://www.scipy-lectures.org/

https://www.coursera.org/learn/machine-learning

http://www.kdnuggets.com/2016/11/top-20-python-machine-learning-open-source-updated.html

http://www.kdnuggets.com/2015/11/statistical-view-deep-learning.html

http://www.kdnuggets.com/2015/11/machine-learning-apis-data-science.html

http://www.kdnuggets.com/2015/10/top-arxiv-deep-learning-papers-explained.html

http://www.kdnuggets.com/2014/11/9-must-have-skills-data-scientist.html

http://www.kdnuggets.com/2015/10/neural-network-python-tutorial.html

http://nbviewer.jupyter.org/gist/yoavg/d76121dfde2618422139

https://sites.google.com/site/shahriarinia/home/ai/machine-learning

https://github.com/udibr/headlines

https://sites.google.com/a/colorado.edu/2016-naacl-ws-human-computer-qa/shared-task

http://www.clips.ua.ac.be/pages/pattern

https://github.com/predictors/iris_flower_classifier_demo

http://kelvinxu.github.io/projects/capgen.html

http://www.autonlab.org/tutorials/

http://www.autonlab.org/tutorials/list.html

https://github.com/NNBlocks/NNBlocks/tree/master/nnb

https://github.com/zer0n/deepframeworks

http://www.isi.edu/view_our_work/open-source_software/

http://jeffhuang.com/search_query_logs.html

http://jsoup.org/

http://www.cs.cmu.edu/~mfaruqui/soft.html- list of datasets and tools mantained by Manaal Faruqui

http://flowingdata.com/2015/07/21/download-data-for-1-7-billion-reddit-comments/

http://www.hlt.utdallas.edu/~sajib/multi-clusterings.html

https://www.trustpilot.com/

https://www.quora.com/Where-can-I-find-large-datasets-open-to-the-public

liblinear

http://deeplearning.net/reading-list/

http://www.youtube.com/playlist?list=PL5-da3qGB5ICeMbQuqbbCOQWcS6OYBr5A

http://www.iro.umontreal.ca/~lisa/twiki/bin/view.cgi/Public/PublicDatasets

http://www.iro.umontreal.ca/~lisa/twiki/bin/view.cgi/Public/WebHome

http://www.iro.umontreal.ca/~lisa/twiki/bin/view.cgi/Public/ListDatasets

http://www.denizyuret.com/2015/03/alec-radfords-animations-for.html

http://www.denizyuret.com/2015/02/beginning-deep-learning-with-500-lines.html

http://www.denizyuret.com/2014/02/machine-learning-in-5-pictures.html

http://www.denizyuret.com/2014/11/some-starting-points-for-deep-learning.html

http://www.denizyuret.com/2015/06/julia-neural-nets-parallelism-and.html

http://www.denizyuret.com/2014/05/how-to-learn-about-deep-learning.html

http://blogs.scientificamerican.com/sa-visual/unveiling-the-hidden-layers-of-deep-learning/

http://deeplearning.net/tutorial/deeplearning.pdf

https://www.tastehit.com/blog/google-deepmind-alphago-how-it-works/

http://www.nature.com/nature/journal/v529/n7587/pdf/nature16961.pdf

https://gogameguru.com/i/2016/03/deepmind-mastering-go.pdf

AlphaGo

https://github.com/cgpotts/annualreview-complearning

https://github.com/cgpotts/cs224u

http://www.kdnuggets.com/2016/06/review-deep-learning-models.html

http://www.kdnuggets.com/2016/06/intro-scientific-python-matplotlib.html

http://www.kdnuggets.com/2016/05/machine-learning-key-terms-explained.html

http://www.kdnuggets.com/2016/05/implementing-neural-networks-javascript.html

http://www.kdnuggets.com/2016/04/deep-learning-neural-networks-overview.html

http://www.kdnuggets.com/2016/04/top-10-ipython-nb-tutorials.html

http://www.kdnuggets.com/2016/04/holding-your-hand-neural-network-part-1.html

http://www.kdnuggets.com/2016/04/holding-your-hand-neural-network-part-2.html

http://www.kdnuggets.com/2016/04/datacamp-learning-python-data-analysis-data-science.html

http://www.kdnuggets.com/2016/04/pocket-guide-data-science.html

http://www.kdnuggets.com/2016/04/delta-deep-learning-from-30000-feet.html

http://www.kdnuggets.com/2016/04/basics-gpu-computing-data-scientists.html

http://www.kdnuggets.com/2016/03/must-know-tips-deep-learning-part-1.html

http://www.kdnuggets.com/2016/03/must-know-tips-deep-learning-part-2.html

http://www.kdnuggets.com/2016/02/tree-kernels-quantifying-similarity-tree-structured-data.html

http://www.kdnuggets.com/2016/02/dezyre-ibm-watson-taking-world.html

http://www.kdnuggets.com/2016/02/dato-introduction-text-analytics-sherlock-holmes.html

http://www.kdnuggets.com/2016/01/implementing-your-own-knn-using-python.html

http://www.kdnuggets.com/2016/01/learning-to-code-neural-networks.html

http://www.kdnuggets.com/2016/01/seven-steps-deep-learning.html

http://www.kdnuggets.com/2015/12/top-10-deep-learning-tips-tricks.html

http://www.kdnuggets.com/2015/12/how-do-neural-networks-learn.html

http://www.kdnuggets.com/2015/11/statistical-view-deep-learning.html

http://www.kdnuggets.com/2015/10/neural-network-python-tutorial.html

http://www.kdnuggets.com/2015/07/good-data-science-machine-learning-cheat-sheets.html

http://www.kdnuggets.com/2015/06/why-does-deep-learning-work.html

http://www.kdnuggets.com/2015/06/visualize-facebook-network.html

http://www.kdnuggets.com/2015/05/top-10-data-mining-algorithms-explained.html

http://www.kdnuggets.com/2015/03/talking-machine-deep-learning-gurus-p1.html

http://www.kdnuggets.com/2015/03/talking-machine-deep-learning-gurus-p2.html

http://www.kdnuggets.com/2015/03/deep-learning-text-understanding-from-scratch.html

http://www.kdnuggets.com/2015/03/deep-learning-curse-dimensionality-autoencoders.html

http://www.kdnuggets.com/2015/03/juergen-schmidhuber-ama-principles-intelligence-machine-learning.html

http://www.kdnuggets.com/2015/03/machine-learning-data-science-common-mistakes.html

http://www.kdnuggets.com/2015/02/rework-deep-learning-summit-san-francisco-january-videos-presentations.html

http://www.kdnuggets.com/2015/01/metamind-ibm-watson-analytics-microsoft-azure-machine-learning.html

http://www.kdnuggets.com/2015/01/deep-learning-explanation-what-how-why.html http://www.kdnuggets.com/2014/05/guide-to-data-science-cheat-sheets.html

http://nbviewer.jupyter.org/github/rhiever/Data-Analysis-and-Machine-Learning-Projects/blob/master/example-data-science-notebook/Example%20Machine%20Learning%20Notebook.ipynb

http://www.analyticsvidhya.com/blog/2015/09/full-cheatsheet-machine-learning-algorithms/

https://github.com/rhiever/dive-into-machine-learning/blob/master/README.md

old

https://class.coursera.org/nlpintro-001/forum/thread?thread_id=97

http://web.eecs.umich.edu/~radev/dlnlp/list.txt

http://pybrain.org/

http://nbviewer.jupyter.org/

http://deeplearning.net/software_links/ - Other deep learning tools (mixed general and specific)

http://deeplearning.net/tutorial/lstm.html

http://deeplearning.net/datasets/ - list of datasets maintained by deeplearning.net

http://deeplearning.net/software/pylearn2/ http://deeplearning.net/tutorial/mlp.html

http://karpathy.github.io/2015/10/25/selfie/

https://pypi.python.org/pypi/polyglot Polyglot

http://stanford.edu/~lmthang/bivec/ bivec

http://www.cntk.ai/ cntk

https://devblogs.nvidia.com/parallelforall/deep-learning-nutshell-core-concepts/

https://triangleinequality.wordpress.com/2014/03/27/neural-networks-part-1/

https://triangleinequality.wordpress.com/2014/03/31/neural-networks-part-2/

pyfst

https://iamtrask.github.io/2014/11/23/harry-potter/

https://projects.propublica.org/graphics/data-institute-2016

http://www.scipy-lectures.org/

https://docs.google.com/spreadsheets/d/1rO3cYZrrIKNMH9poTQEGhKUSOoS3zML0AjDItoGWzOQ/edit#gid=0

https://github.com/jakevdp/PythonDataScienceHandbook

https://lvdmaaten.github.io/tsne/

https://mxnet.readthedocs.io/en/latest/

http://mscoco.org/dataset/#overview

https://github.com/eske/multivec

https://github.com/jdwittenauer/ipython-notebooks

http://www.johnwittenauer.net/machine-learning-exercises-in-python-part-1/

http://www.johnwittenauer.net/machine-learning-exercises-in-python-part-2/

http://www.johnwittenauer.net/machine-learning-exercises-in-python-part-3/

http://www.johnwittenauer.net/machine-learning-exercises-in-python-part-4/

http://www.johnwittenauer.net/machine-learning-exercises-in-python-part-5/

http://www.johnwittenauer.net/machine-learning-exercises-in-python-part-6/

http://www.johnwittenauer.net/machine-learning-exercises-in-python-part-7/

http://www.johnwittenauer.net/machine-learning-exercises-in-python-part-8/

http://www.johnwittenauer.net/assignments-from-googles-deep-learning-class-posted/

http://www.johnwittenauer.net/an-intro-to-probablistic-programming/

http://scott.fortmann-roe.com/docs/BiasVariance.html

https://snap.stanford.edu/data/

https://research.googleblog.com/2015/06/a-multilingual-corpus-of-automatically.html

http://www.cs.cmu.edu/~ark/personas/

https://www.technologyreview.com/s/602094/ais-language-problem/

http://spacy.io/

https://www.wordnik.com/

http://onlinebooks.library.upenn.edu/webbin/gutbook/lookup?num=3202

https://github.com/davidjurgens/crown

http://takelab.fer.hr/sts/ http://clic.cimec.unitn.it/composes/toolkit/

http://babelnet.org/

http://clic.cimec.unitn.it/dm/

https://github.com/dkpro/dkpro-similarity

http://leon.bottou.org/projects/sgd

https://rawgit.com/dpressel/Meetups/master/nlp-meetup-2016-02-25/presentation.html

https://rawgit.com/dpressel/Meetups/master/nlp-meetup-2016-04-27/presentation.html

https://github.com/dpressel/baseline

https://ai.stanford.edu/~ajoulin/code/nn.zip

https://github.com/facebookresearch/fastText

http://metaoptimize.com/projects/wordreprs/

http://rs.io/100-interesting-data-sets-for-statistics/

http://deeplearning.net/software/pylearn2/

https://github.com/lisa-groundhog/GroundHog

https://github.com/fh295/GroundHog

https://ift6266h15.wordpress.com/category/lectures/page/3/

http://cogcomp.cs.illinois.edu/page/resource_view/49

http://deeplearning.net/software_links/

http://www-lium.univ-lemans.fr/cslm/

https://pypi.python.org/pypi/textteaser/0.3

https://pypi.python.org/pypi/boilerpipe

https://pypi.python.org/pypi/goose-extractor/

https://pypi.python.org/pypi/nameparser/0.3.9

https://wit3.fbk.eu/

Freebase

Freebase relations corpus

http://bamos.github.io/2016/08/09/deep-completion/

http://www.techrepublic.com/article/ibm-watson-machine-learns-the-art-of-writing-a-good-headline/

https://www.cs.ox.ac.uk/people/nando.defreitas/machinelearning/

https://techcrunch.com/2016/08/18/facebooks-artificial-intelligence-research-lab-releases-open-source-fasttext-on-github/

https://github.com/facebookresearch/fastText

http://www.thespermwhale.com/jaseweston/icml2016/

http://www.paddlepaddle.org/

https://www.cntk.ai/

https://github.com/swiseman/nn_coref

https://github.com/wojciechz/learning_to_execute

http://www.jflap.org/

https://oaqa.github.io/

https://pypi.python.org/pypi/quepy/

http://pyke.sourceforge.net/

https://bitbucket.org/yoavartzi/spf

http://www.openfst.org/twiki/bin/view/FST/WebHome

https://en.wikipedia.org/wiki/Forward%E2%80%93backward_algorithm

https://github.com/jwieting/iclr2016

https://github.com/saffsd/langid.py

https://bitbucket.org/richardpenman/sitescraper/

http://www1.icsi.berkeley.edu/~demelo/etymwn/

https://github.com/thinkzhou/PCFG

https://github.com/percyliang/brown-cluster

https://github.com/mheilman/tan-clustering

http://christos-c.com/bible/

http://www.eat.rl.ac.uk/ http://w3.usf.edu/FreeAssociation/

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

http://colah.github.io/posts/2015-09-NN-Types-FP/

http://colah.github.io/posts/2015-01-Visualizing-Representations/

https://research.googleblog.com/2015/06/inceptionism-going-deeper-into-neural.html

http://colah.github.io/posts/2014-12-Groups-Convolution/

http://colah.github.io/posts/2014-07-FFN-Graphs-Vis/

http://colah.github.io/posts/2015-02-DataList-Illustrated/

http://colah.github.io/posts/2015-09-Visual-Information/

http://www.wordspy.com/

https://stats.stackexchange.com/questions/89030/rand-index-calculation

https://github.com/NervanaSystems/neon

https://sites.google.com/site/nirajatweb/home/interactive-tutorials

https://github.com/karpathy/paper-notes/blob/master/wikireading.md

http://image-net.org/small/download.php

https://github.com/salestock/fastText.py

http://nlpers.blogspot.com/2016/07/decoding-neural-representations.html

https://pmirla.github.io/2016/06/05/gradient-explanation.html

https://blog.fastforwardlabs.com/2016/08/12/introducing-variational-autoencoders-in-prose-and-code.html

http://www.gavagai.se/distributional_semantics.php

https://github.com/jakevdp/PythonDataScienceHandbook

https://medium.com/@philjama/how-tensors-advance-human-technology-3831bff0906#.x1pg63new

http://www.kdnuggets.com/2016/05/implement-machine-learning-algorithms-scratch.html

https://www.technologyreview.com/s/602344/the-extraordinary-link-between-deep-neural-networks-and-the-nature-of-the-universe/

http://nlp.stanford.edu/projects/histwords/

https://nlp.stanford.edu/projects/socialsent/

https://github.com/ipod825/keraflow

http://videolectures.net/deeplearning2016_cho_language_understanding/

http://www.kdnuggets.com/2013/12/top-datasets-on-reddit.html

https://github.com/baidu/paddle

http://veredshwartz.blogspot.co.il/2016/08/crowdsourcing-for-nlp.html

https://github.com/codalab/codalab-worksheets/wiki

https://github.com/kbalog/russir2016-el

http://arkitus.com/patterns-for-research-in-machine-learning/

https://www.reddit.com/r/MachineLearning/comments/515dus/kdd_panel_is_deep_learning_the_new_42/

https://www.linkedin.com/pulse/google-nli-kill-market-linguistic-apis-review-yuri-kitin

http://michal.sustr.sk/blog/outlier-analysis/

https://research.googleblog.com/2016/08/tf-slim-high-level-library-to-define.html

https://research.googleblog.com/2016/03/train-your-own-image-classifier-with.html

https://radimrehurek.com/gensim/models/phrases.html

http://alt.qcri.org/semeval2017/

http://swoogle.umbc.edu/SimService/index.html

https://github.com/dlwh/epic

https://github.com/dlwh/breeze

https://github.com/jacobandreas/pragma

https://github.com/jacobandreas/nmn2

https://github.com/uclmr/acl2015tutorial

http://phrasesinenglish.org/#

http://www.natcorp.ox.ac.uk/

http://www.uow.edu.au/~dlee/software.htm

https://colinmorris.github.io/blog/dreaming-rbms

https://colinmorris.github.io/blog/rbm-sampling

https://iamtrask.github.io/2015/07/28/dropout/

http://press.liacs.nl/mirflickr/

http://www.mcmchandbook.net/HandbookSampleChapters.html

https://www.reddit.com/r/MachineLearning/comments/54bpsb/yann_lecun_deep_learning_and_the_future_of_ai/

https://github.com/ryankiros/neural-storyteller

https://github.com/andreasvc/seekaywhy

http://text-processing.com/demo/

http://odur.let.rug.nl/~vannoord/Fsa/

http://gawron.sdsu.edu/compling/tools/python/

https://github.com/dennybritz/representation-learning

http://videolectures.net/deeplearning2016_cho_language_understanding/

https://github.com/UKPLab/deeplearning4nlp-tutorial

https://github.com/dennybritz/startupreadings

https://github.com/jsvine/markovify

https://github.com/dmlc/mxnet/tree/master/example

http://www.cl.cam.ac.uk/~sc609/java-candc.html

https://bitbucket.org/yoavartzi/spf

https://github.com/andialbrecht/sqlparse

https://github.com/julianser/hed-dlg-truncated

https://github.com/dmorr-google/wiki-reading

https://cs.umd.edu/~miyyer/qblearn/

https://github.com/sivareddyg/graph-parser

https://github.com/donglixp/lang2logic

https://github.com/sinantie/Generator

http://nlpado.de/~sebastian/software/dv.shtml

https://www.linkedin.com/pulse/google-nli-kill-market-linguistic-apis-review-yuri-kitin

https://github.com/dlwh/puck/

http://www.scalanlp.org/

http://scott.fortmann-roe.com/docs/BiasVariance.html

http://blog.webkid.io/datasets-for-machine-learning/

https://github.com/mlbright/edmonds

https://www.analyticsvidhya.com/blog/2016/08/evolution-core-concepts-deep-learning-neural-networks/

https://www.analyticsvidhya.com/blog/2016/09/40-interview-questions-asked-at-startups-in-machine-learning-data-science/

https://www.analyticsvidhya.com/blog/2016/05/19-data-science-tools-for-people-dont-understand-coding/

https://www.analyticsvidhya.com/blog/2016/03/introduction-deep-learning-fundamentals-neural-networks/

https://www.analyticsvidhya.com/blog/2016/02/free-read-books-statistics-mathematics-data-science/

https://www.analyticsvidhya.com/blog/2016/01/complete-tutorial-learn-data-science-python-scratch-2/

https://www.analyticsvidhya.com/blog/2016/01/10-popular-tv-shows-data-science-artificial-intelligence/

https://www.analyticsvidhya.com/blog/2016/01/12-pandas-techniques-python-data-manipulation/

https://www.analyticsvidhya.com/blog/2015/12/started-graphlab-python/

https://www.analyticsvidhya.com/blog/2015/11/lifetime-lessons-20-data-scientist-today/

https://www.analyticsvidhya.com/blog/2015/11/7-watch-documentaries-statistics-machine-learning/

https://www.analyticsvidhya.com/blog/2016/09/40-interview-questions-asked-at-startups-in-machine-learning-data-science/

https://github.com/sriniiyer/codenn

https://github.com/miyyer/rmn

https://research.facebook.com/research/babi/

http://rtw.ml.cmu.edu/rtw/

http://www.hlt.utdallas.edu/~altaf/cherrypicker/index.html

http://ai-on.org/

https://github.com/jwieting/charagram

http://sebastianruder.com/optimizing-gradient-descent/

http://blog.christianperone.com/2011/09/machine-learning-text-feature-extraction-tf-idf-part-i/

http://www.isi.edu/natural-language/software/nplm/

http://www.isi.edu/natural-language/software/EUREKA.tar.gz

https://github.com/dmorr-google/wiki-reading

https://github.com/jacobeisenstein/gt-nlp-class/tree/master/notes

https://www.youtube.com/playlist?list=PL6Xpj9I5qXYEcOhn7TqghAJ6NAPrNmUBH

https://ujjwalkarn.me/2016/08/09/quick-intro-neural-networks/

https://developers.google.com/edu/python/

https://www.youtube.com/watch?v=tKTZoB2Vjuk

http://opennlp.sourceforge.net/projects.html

https://github.com/ai-ku/wvec

https://github.com/knowitall/reverb/

https://github.com/dmcc/PyStanfordDependencies

https://github.com/proycon/pynlpl

https://github.com/machinalis/yalign

http://textblob.readthedocs.io/en/dev/

http://www.clips.ua.ac.be/pattern

http://nbviewer.jupyter.org/github/fbkarsdorp/doc2vec/blob/master/doc2vec.ipynb

https://github.com/deepmind/rc-data/

http://textblob.readthedocs.io/en/dev/

https://github.com/proycon/pynlpl

https://github.com/proycon/python-ucto

https://github.com/explosion/spaCy

https://github.com/dasmith/stanford-corenlp-python

https://pypi.python.org/pypi/editdistance

https://github.com/Lasagne/Lasagne

https://github.com/ContinuumIO/topik

https://github.com/pybrain/pybrain

https://github.com/echen/restricted-boltzmann-machines

https://github.com/jmschrei/yahmm/

https://github.com/andersbll/deeppy

https://github.com/dmlc/mxnet

https://networkx.github.io/

http://igraph.org/python/

http://pandas.pydata.org/

https://github.com/pymc-devs/pymc

https://github.com/ipython/ipython/wiki/A-gallery-of-interesting-IPython-Notebooks

https://github.com/ogrisel/notebooks

https://github.com/donnemartin/data-science-ipython-notebooks

http://www.karsdorp.io/python-course/

https://github.com/vinta/awesome-python

https://taku910.github.io/crfpp/

http://stanfordnlp.github.io/CoreNLP/

http://nlp.stanford.edu/phrasal/

https://github.com/apache/mahout

http://meka.sourceforge.net/

https://sourceforge.net/p/lemur/wiki/RankLib/

https://github.com/twitter/twitter-text

https://www.codecademy.com/learn/python

http://www.dataschool.io/15-hours-of-expert-machine-learning-videos/

http://www.ted.com/playlists/310/talks_on_artificial_intelligen

http://outlace.com/Simple-Genetic-Algorithm-in-15-lines-of-Python/

http://outlace.com/Simple-Genetic-Algorithm-Python-Addendum/

https://github.com/trevorstephens/gplearn

http://alexminnaar.com/

https://github.com/soulmachine/machine-learning-cheat-sheet

https://github.com/rouseguy/intro2deeplearning

http://www.kdnuggets.com/2015/10/top-arxiv-deep-learning-papers-explained.html

https://www.toptal.com/machine-learning/an-introduction-to-deep-learning-from-perceptrons-to-deep-networks

http://ufldl.stanford.edu/tutorial/supervised/MultiLayerNeuralNetworks/

http://deeplearning.net/tutorial/mlp.html#mlp

https://deeplearning4j.org/restrictedboltzmannmachine.html

https://deeplearning4j.org/deepautoencoder.html

http://deeplearning.net/tutorial/dA.html

https://github.com/aikorea/awesome-rl

http://stackoverflow.com/questions/1859554/what-is-entropy-and-information-gain

https://github.com/kjw0612/awesome-random-forest

https://github.com/dpressel/baseline

https://github.com/karpathy/neuraltalk

https://github.com/Microsoft/DMTK

https://github.com/PaddlePaddle/Paddle

https://sourceforge.net/p/rnnl/wiki/Home/

https://www.toptal.com/machine-learning/an-introduction-to-deep-learning-from-perceptrons-to-deep-networks

https://github.com/josephmisiti/machine-learning-module

https://github.com/karpathy/char-rnn

https://github.com/idio/wiki2vec

https://gist.github.com/francoiseprovencher/83c595531177ac88e3c0

https://github.com/fbkarsdorp/python-course

http://www.akbarian.org/notes/text-mining-nlp-python/

https://www.nervanasys.com/demystifying-deep-reinforcement-learning/

https://www.quora.com/Why-does-LDA-work/answer/James-McInerney-1

http://confusedlanguagetech.blogspot.com/2012/07/jordan-boyd-graber-and-philip-resnik.html

https://www.youtube.com/watch?v=bny8C5bFXDM&feature=share

https://github.com/YerevaNN/translit-rnn

https://github.com/brmson/dataset-factoid-webquestions

https://github.com/mast-group/convolutional-attention

http://www.ling.ohio-state.edu/~elsner.14/resources/chat-manual.html

https://github.com/hiroki13/response-ranking

https://github.com/sleepinyourhat/vector-entailment/releases/

https://code.google.com/archive/p/clml/

http://deeplearning.net/tutorial/rnnslu.html

https://pypi.python.org/pypi/TheanoLM

http://www.kyunghyuncho.me/home/code

http://deeplearning.net/demos/

https://github.com/sq6ra/

https://devblogs.nvidia.com/parallelforall/deep-learning-nutshell-core-concepts/

http://blocks.readthedocs.io/en/latest/index.html

https://github.com/karlmoritz/bicvm

http://toritris.weebly.com/perceptron-5-xor-how–why-neurons-work-together.html

https://github.com/phanein/deepwalk

http://tflearn.org/

https://www.analyticsvidhya.com/blog/2016/12/21-deep-learning-videos-tutorials-courses-on-youtube-from-2016

https://www.analyticsvidhya.com/blog/2016/03/13-machine-learning-data-science-startups-combinator-winter-2016/

https://www.analyticsvidhya.com/blog/2016/11/building-a-machine-learning-deep-learning-workstation-for-under-5000/

http://clic.cimec.unitn.it/~georgiana.dinu/down/

https://github.com/NickShahML/tensorflow_with_latest_papers

https://chatbotslife.com/resnets-highwaynets-and-densenets-oh-my-9bb15918ee32

https://github.com/amueller/introduction_to_ml_with_python

http://pystruct.github.io/

https://github.com/amueller/word_cloud

https://github.com/ipython-books/cookbook-code

https://github.com/ipython-books/minibook-2nd-code

https://github.com/ipython-books/cookbook-data

https://github.com/ipython-books/minibook-2nd-data

http://news.mit.edu/2016/making-computers-explain-themselves-machine-learning-1028

https://code.facebook.com/posts/384869298519962/artificial-intelligence,-revealed/

https://work.caltech.edu/lectures.html

https://github.com/lajanugen/NLP_from_scratch/blob/master/README.md

http://www.cs.pomona.edu/~dkauchak/mt-tutorial/

https://github.com/songrotek/Deep-Learning-Papers-Reading-Roadmap

http://www.denizyuret.com/2014/11/some-starting-points-for-deep-learning.html

https://github.com/rbouckaert/DensiTree

https://simons.berkeley.edu/talks/tutorial-deep-learning

https://adeshpande3.github.io/adeshpande3.github.io/Deep-Learning-Research-Review-Week-1-Generative-Adversarial-Nets

https://adeshpande3.github.io/adeshpande3.github.io/Deep-Learning-Research-Review-Week-2-Reinforcement-Learning

https://adeshpande3.github.io/adeshpande3.github.io/The-9-Deep-Learning-Papers-You-Need-To-Know-About.html

http://videolectures.net/deeplearning2016_montreal/ https://sites.google.com/site/deeplearningsummerschool2016/speakers

http://allenai.org/data.html

http://www.kdnuggets.com/2017/01/great-collection-clean-machine-learning-algorithms.html

https://github.com/lium-lst/nmtpy

https://github.com/tensorflow/fold

http://thenewstack.io/reinforcement-learning-ready-real-world/

https://github.com/stanfordnlp/spinn

https://github.com/yogarshi/bisparse

http://allenai.org/data.htmlallenai.org

https://github.com/deepmind/lab

https://arnabgho.github.io/Contextual-RNN-GAN/

https://github.com/arnabgho/Contextual-RNN-GAN

https://kheafield.com/code/kenlm/

http://www.clips.ua.ac.be/pattern

https://github.com/clips/pattern

https://medium.com/%40devnag/generative-adversarial-networks-gans-in-50-lines-of-code-pytorch-e81b79659e3f#.jws4akshy

http://sebastianruder.com/word-embeddings-softmax/

https://github.com/hoytak/pyksvd

https://sites.google.com/site/rmyeid/projects/polyglot

https://medium.com/startup-grind/fueling-the-ai-gold-rush-7ae438505bc2#.dycvqpypw

http://www.statmt.org/lm-benchmark/

https://bitbucket.org/melsner/browncoherence

http://www.kdnuggets.com/2016/08/begineers-guide-neural-networks-r.html

http://www.kdnuggets.com/2015/11/seven-steps-machine-learning-python.html

http://www.kdnuggets.com/2016/10/beginners-guide-neural-networks-python-scikit-learn.html

https://github.com/adeshpande3/Pandas-Tutorial/blob/master/Pandas%20Tutorial.ipynb

http://www.kdnuggets.com/2016/11/parallelism-machine-learning-gpu-cuda-threading.html

http://www.kdnuggets.com/2016/10/jupyter-notebook-best-practices-data-science.html

https://algobeans.com/2016/11/03/artificial-neural-networks-intro2/

https://algobeans.com/2016/05/19/build-a-deep-learning-box/

https://algobeans.com/2016/04/12/network-graphs-where-will-your-country-stand-in-world-war-iii/

https://algobeans.com/2016/03/13/how-do-computers-recognise-handwriting-using-artificial-neural-networks/

http://www.cl.cam.ac.uk/~fh295/simlex.html http://www.cl.cam.ac.uk/~fh295/dicteval.html

http://www.kdnuggets.com/2017/02/natural-language-processing-key-terms-explained.html

https://metamind.io/research/new-neural-network-building-block-allows-faster-and-more-accurate-text-understanding/

https://github.com/tensorflow/models/tree/master/neural_programmer

http://www.cis.lmu.de/~sascha/AutoExtend/

---

NEW

https://github.com/facebookresearch/fastText/blob/master/pretrained-vectors.md

https://jaan.io/what-is-variational-autoencoder-vae-tutorial/

https://code.google.com/archive/p/morphisto/

https://github.com/mesnilgr/nbsvm

https://bitbucket.org/melsner/

https://github.com/ciprian-chelba/1-billion-word-language-modeling-benchmark

https://metamind.io/research/the-wikitext-long-term-dependency-language-modeling-dataset

https://github.com/nyu-dl/Intro_to_ML_Lecture_Note

https://github.com/nyu-dl/NLP_DL_Lecture_Note

https://github.com/clab/dynet

http://www.mmds.org/

http://infolab.stanford.edu/~ullman/mmds/book.pdf

https://github.com/facebook/MemNN/tree/master/DBLL

https://github.com/tkipf/gcn

https://github.com/deepmind/rc-data/

https://github.com/WING-NUS/scisumm-corpus

http://www.asimovinstitute.org/neural-network-zoo/

https://worksheets.codalab.org/

https://google.github.io/seq2seq/

https://github.com/google/seq2seq

https://github.com/rguthrie3/DeepLearningForNLPInPytorch/blob/master/README.md

https://github.com/yunjey/pytorch-tutorial/blob/master/README.md

https://www.youtube.com/playlist?list=PL613dYIGMXoZBtZhbyiBqb0QtgK6oJbpm

https://metamind.io/research/learning-when-to-skim-and-when-to-read

https://github.com/oxford-cs-deepnlp-2017/lectures

https://github.com/tensorflow/models

https://github.com/achillesrasquinha/bulbea

https://www.youtube.com/watch?v=ogrJaOIuBx4

https://www.tensorflow.org/tutorials/recurrent

http://yerevann.com/a-guide-to-deep-learning/

http://rll.berkeley.edu/deeprlcourse/

https://www.cs.ox.ac.uk/people/nando.defreitas/machinelearning/

http://www.jeremydjacksonphd.com/deep-learning-resources/

https://work.caltech.edu/telecourse.html

http://cs231n.github.io/

http://cs231n.github.io/neural-networks-case-study/

http://cs231n.github.io/assignments2016/assignment1/

http://cs231n.github.io/assignments2016/assignment2/

http://cs231n.github.io/assignments2016/assignment3/

http://blog.paralleldots.com/data-scientist/new-deep-learning-datasets-data-scientists/

http://pmb.let.rug.nl/

https://data.quora.com/First-Quora-Dataset-Release-Question-Pairs

https://github.com/dmorr-google/wiki-reading#wikireading-data

http://hcp.sysu.edu.cn/lip/index.php

https://research.googleblog.com/2017/03/an-upgrade-to-syntaxnet-new-models-and.html

https://archive.ics.uci.edu/ml/datasets/NIPS+Conference+Papers+1987-2015

https://medium.com/%40devnag/pointer-networks-in-tensorflow-with-sample-code-14645063f264#.kilqlgdpj

https://github.com/kedz/sumpn

https://github.com/kedz/learn2sum

https://github.com/kedz/sumpy

https://github.com/kedz/cuttsum

https://www.socher.org

https://code.google.com/p/jacana/

http://nal.co/DCNN

http://www.bart-coref.org

https://github.com/MateLabs/Tensorflow-setup-scripts

http://nlp.stanford.edu/software/dcoref.shtml

http://nlp.cs.berkeley.edu/projects/coref.shtml

http://www.cs.utah.edu/nlp/reconcile/

http://cogcomp.cs.illinois.edu/page/software_view/Coref

http://nlp.stanford.edu/sentiment/

http://alchemy.cs.washington.edu/usp/

http://ttic.uchicago.edu/~mbansal/data/syntacticEmbeddings.zip

https://github.com/rasbt/deep-learning-book

http://www.wordvectors.org/web-eacl14-vectors/de-projected-en-512.txt.gz

http://natureofcode.com/book/

https://github.com/shiffman/The-Nature-of-Code-Examples

http://www.r2d3.us/visual-intro-to-machine-learning-part-1/

http://www.wordvectors.org/lorelei.php

http://colah.github.io/posts/2014-10-Visualizing-MNIST/

https://blog.dbrgn.ch/2013/3/26/perceptrons-in-python/

https://www.youtube.com/user/sentdex/featured

http://128.2.220.95/multilingual/data/

https://www.youtube.com/watch?v=u4alGiomYP4&app=desktop

https://cloud.google.com/blog/big-data/2017/01/learn-tensorflow-and-deep-learning-without-a-phd

https://github.com/oxford-cs-deepnlp-2017/lectures

http://www.kdnuggets.com/2017/02/python-deep-learning-frameworks-overview.html

https://developer.nvidia.com/how-to-cuda-python

http://www.kdnuggets.com/2015/06/top-20-python-machine-learning-open-source-projects.html

http://cmusphinx.sourceforge.net/wiki/tutorial

http://www.kdnuggets.com/2016/11/deep-learning-research-review-reinforcement-learning.html

http://www.kdnuggets.com/2016/10/deep-learning-research-review-generative-adversarial-networks.html

https://github.com/golastmile/rasa_nlu

https://www.youtube.com/playlist?list=PLOU2XLYxmsIIuiBfYad6rFYQU_jL2ryal

https://blog.acolyer.org/2017/03/23/recurrent-neural-network-models/

https://github.com/terryum/awesome-deep-learning-papers

https://github.com/davidjurgens/crown

https://github.com/dirkhovy/emtutorial

https://github.com/dirkhovy/python_for_linguists

http://distill.pub/2016/misread-tsne/

http://distill.pub/2016/handwriting/

https://www.linkedin.com/pulse/deep-learning-stock-price-prediction-explained-joe-ellsworth

http://dataconomy.com/2017/02/mathematics-machine-learning/

https://www.jetbrains.com/pycharm/

http://ucanalytics.com/blogs/intuitive-machine-learning-gradient-descent-simplified/

http://www.datasciencecentral.com/profiles/blogs/scikit-learn-tutorial-series

http://dood.al/pinktrombone/

https://blog.acolyer.org/2017/03/24/a-miscellany-of-fun-deep-learning-papers/

https://blog.openai.com/adversarial-example-research/

https://github.com/elikip/bist-parser

https://github.com/elikip/htparser

https://github.com/elikip/SimpleParser https://github.com/nicolaifsf/Installing-Tensorflow-with-GPU

https://www.youtube.com/watch?v=p1EQbdMfbPQ

Noah Smith

https://athena.brynmawr.edu/jupyter/hub/josh/public/BioCS115/Lecture_Notes

https://athena.brynmawr.edu/jupyter/hub/josh/public/BioCS115/Lecture_Notes/2016-04-06_Artificial_Neural_networks.ipynb

https://medium.com/safegraph/a-non-technical-introduction-to-machine-learning-b49fce202ae8 https://medium.com/@gk_/tensorflow-demystified-80987184faf7

https://medium.com/%40ageitgey/try-deep-learning-in-python-now-with-a-fully-pre-configured-vm-1d97d4c3e9b

https://code.facebook.com/posts/1373769912645926/faiss-a-library-for-efficient-similarity-search/

https://github.com/hangtwenty/dive-into-machine-learning

https://github.com/yandexdataschool/Practical_RL

https://github.com/jaredthecoder/BioPy

https://github.com/GaelVaroquaux/scikit-learn-tutorial

https://github.com/jvns/pandas-cookbook

https://github.com/AllenDowney/DataScience

https://github.com/hangtwenty/dive-into-machine-learning

https://github.com/atmb4u/data-driven-code

https://github.com/justmarkham/scikit-learn-videos

http://www.karsdorp.io/python-course/

Python for the humanities

https://github.com/donnemartin/data-science-ipython-notebooks

https://github.com/ogrisel/notebooks

https://github.com/rasbt/pattern_classification

https://www.youtube.com/watch?v=nRBnh4qbPHI

https://www.oreilly.com/learning/not-another-mnist-tutorial-with-tensorflow

http://data8.org/

https://www.youtube.com/playlist?list=PLA83b1JHN4lw-BDpu4r__T8cpnvLIJHA-

http://www0.cs.ucl.ac.uk/staff/d.silver/web/Teaching.html

http://rll.berkeley.edu/deeprlcourse/ https://github.com/madhusudancs/sentiment-analyzer

https://www.youtube.com/watch?v=ElmBrKyMXxs

https://code.facebook.com/posts/384869298519962/artificial-intelligence-revealed/

https://www.youtube.com/watch?v=3JQ3hYko51Y

https://rare-technologies.com/deep-learning-with-word2vec-and-gensim/

https://rare-technologies.com/word2vec-in-python-part-two-optimizing/

https://rare-technologies.com/word2vec-tutorial/

https://rare-technologies.com/tutorial-on-mallet-in-python/

https://rare-technologies.com/making-sense-of-word2vec/

https://rare-technologies.com/doc2vec-tutorial/

https://rare-technologies.com/text-summarization-with-gensim/

https://rare-technologies.com/radim-gensim-and-rare-technologies/

https://rare-technologies.com/fasttext-and-gensim-word-embeddings/

https://rare-technologies.com/wordrank-embedding-crowned-is-most-similar-to-king-not-word2vecs-canute/

https://rare-technologies.com/gensim-switches-to-semantic-versioning/

https://rare-technologies.com/rrp-1-tomas-mikolov-on-word2vec-and-ai-research-at-microsoft-google-facebook/

https://rare-technologies.com/new-gensim-feature-author-topic-modeling-lda-with-metadata/

https://rare-technologies.com/gensim-switches-to-semantic-versioning/

https://rare-technologies.com/author-topic-models-why-i-am-working-on-a-new-implementation/

https://blog.openai.com/unsupervised-sentiment-neuron/

https://medium.com/%40rayalez/list-of-the-best-resources-to-learn-the-foundations-of-artificial-intelligence-934dbce5939

https://magenta.tensorflow.org/nsynth https://github.com/deepmind/sonnet

https://research.googleblog.com/2017/03/announcing-audioset-dataset-for-audio.html

https://github.com/aikorea/awesome-rl

https://www.youtube.com/watch?v=ID150Tl-MMw Deep Reinforcement Learning

https://github.com/ayushoriginal/Multi-Document-Summarization

http://mcneela.github.io/machine_learning/2017/03/21/Universal-Approximation-Theorem.html

https://www.youtube.com/watch?v=t5qgjJIBy9g

How to Make a Chatbot - Intro to Deep Learning #12

https://blogs.technet.microsoft.com/machinelearning/2017/04/03/microsoft-updates-its-deep-learning-toolkit/

https://nlpers.blogspot.com/2017/04/structured-prediction-is-not-rl.html

https://github.com/adeshpande3/Generative-Adversarial-Networks

https://medium.com/@gokul_uf/the-anatomy-of-deep-learning-frameworks-46e2a7af5e47

https://www.youtube.com/playlist?list=PLoRl3Ht4JOcdU872GhiYWf6jwrk_SNhz9

Neural Networks for Machine Learning Geoffrey Hinton 2016 https://www.youtube.com/watch?v=fBVEXKp4DIc

How to use Tensorboard https://www.youtube.com/playlist?list=PL3FW7Lu3i5Jsnh1rnUwq_TcylNr7EkRe6

https://conference.scipy.org/scipy2013/tutorial_detail.php?id=109

https://www.analyticsvidhya.com/blog/2016/01/complete-tutorial-learn-data-science-python-scratch-2/

http://ipython-books.github.io/featured-07/

https://chatbotslife.com/deep-learning-in-7-lines-of-code-7879a8ef8cfb

https://gym.openai.com/

http://cs231n.github.io/python-numpy-tutorial/

https://pjreddie.com/darknet/

http://www-bcf.usc.edu/%7Egareth/ISL/

http://www-bcf.usc.edu/%7Egareth/ISL/ISLR%20Sixth%20Printing.pdf

http://statweb.stanford.edu/%7Etibs/ElemStatLearn/printings/ESLII_print10.pdf

http://nlp.stanford.edu/software/

https://kevincodeidea.wordpress.com/2016/02/03/reconstruction-pca-svd-and-autoencoder/

http://thegrandjanitor.com/2016/08/15/learning-deep-learning-my-top-five-resource/

http://cv-tricks.com/tensorflow-tutorial/training-convolutional-neural-network-for-image-classification/

https://medium.com/%40nicolabernini_63880/ml-what-is-the-difference-between-gradient-descent-and-stochastic-gradient-descent-be79ab450ef0

https://theinformationageblog.wordpress.com/2017/04/10/artificial-sentiment-analysis-the-new-achievement-by-openai/

https://www.slideshare.net/CharlesVestur/building-a-performing-machine-learning-model-from-a-to-z

https://www.youtube.com/watch?v=OB1reY6IX-o

https://www.youtube.com/watch?v=BR9h47Jtqyw

https://medium.com/@karpathy/a-peek-at-trends-in-machine-learning-ab8a1085a106

https://nlp.stanford.edu/projects/socialsent/

https://github.com/williamleif/socialsent

https://research.googleblog.com/2017/04/introducing-tf-seq2seq-open-source.html?m=1

https://www.youtube.com/playlist?list=PLlJy-eBtNFt6EuMxFYRiNRS07MCWN5UIA

cs231n https://blog.sourced.tech/post/lapjv/

https://github.com/HarshTrivedi/paraphrase-generation

https://devblogs.nvidia.com/parallelforall/recursive-neural-networks-pytorch/

https://github.com/syncpy/SyncPy

https://medium.com/becoming-human/tensorflow-serving-by-creating-and-using-docker-images-336ca4de8671

https://chatbotnewsdaily.com/since-the-initial-standpoint-of-science-technology-and-ai-scientists-following-blaise-pascal-and-804ac13d8151

https://blog.heuritech.com/2017/04/11/began-state-of-the-art-generation-of-faces-with-generative-adversarial-networks/

https://venturebeat.com/2017/04/02/understanding-the-limits-of-deep-learning/

https://ayearofai.com/rohan-lenny-3-recurrent-neural-networks-10300100899b

https://github.com/hiroki13/response-ranking/tree/master/data/input

https://dnlcrl.github.io/projects/2015/10/10/500-deep-learning-papers-graphviz-python.html

https://dnlcrl.github.io/projects/2015/10/15/500-deep-learning-papers-part-2.html

https://github.com/dnlcrl/PyScholarGraph

https://github.com/dnlcrl/scholar.py

https://github.com/mnielsen/neural-networks-and-deep-learning

https://snap.stanford.edu/snappy/

https://github.com/clab/dynet_tutorial_examples

https://nbviewer.jupyter.org/github/jupyter/notebook/blob/master/docs/source/examples/Notebook/Running%20Code.ipynb

https://nbviewer.jupyter.org/github/ipython/ipython/blob/master/examples/IPython%20Kernel/Index.ipynb

https://nbviewer.jupyter.org/github/ipython/ipython/blob/master/examples/IPython%20Kernel/Custom%20Display%20Logic.ipynb

http://nbviewer.jupyter.org/github/jrjohansson/scientific-python-lectures/tree/master/

https://github.com/rossant/ipython-minibook

https://github.com/rajathkumarmp/Python-Lectures

http://nbviewer.jupyter.org/github/jrjohansson/scientific-python-lectures/blob/master/Lecture-4-Matplotlib.ipynb

http://nbviewer.jupyter.org/github/ehmatthes/intro_programming/blob/master/notebooks/index.ipynb

http://ricardoduarte.github.io/python-for-developers/

https://github.com/yoavram/CS1001.py

http://mbakker7.github.io/exploratory_computing_with_python/

http://nbviewer.jupyter.org/github/lmarti/evolutionary-computation-course/blob/master/AEC.04%20-%20Evolutionary%20Strategies%20and%20Covariance%20Matrix%20Adaptation.ipynb

https://notebooks.azure.com/library/CUED-IA-Computing-Michaelmas

https://github.com/jakevdp/PythonDataScienceHandbook

https://github.com/thomas-haslwanter/statsintro_python

https://github.com/ipython-books/cookbook-code

http://nbviewer.jupyter.org/github/temporaer/tutorial_ml_gkbionics/blob/master/2%20-%20KMeans.ipynb

http://nbviewer.jupyter.org/github/amplab/datascience-sp14/blob/master/hw2/HW2.ipynb

http://nbviewer.jupyter.org/github/masinoa/machine_learning/blob/master/04_Neural_Networks.ipynb

https://github.com/masinoa/machine_learning

https://bitbucket.org/hrojas/learn-pandas

https://github.com/phelps-sg/python-bigdata/blob/master/README.md

https://github.com/sujitpal/statlearning-notebooks

http://nbviewer.jupyter.org/github/ledeprogram/courses/tree/master/

http://nbviewer.jupyter.org/github/jdwittenauer/ipython-notebooks/blob/master/notebooks/ml/ML-Exercise1.ipynb

http://nbviewer.jupyter.org/github/rhiever/Data-Analysis-and-Machine-Learning-Projects/tree/master/

https://github.com/donnemartin/data-science-ipython-notebooks/blob/master/README.md

http://www.karsdorp.io/python-course/

Python for the Humanities

http://nbviewer.jupyter.org/github/rasbt/python_reference/blob/master/tutorials/key_differences_between_python_2_and_3.ipynb

https://github.com/ptwobrussell/Mining-the-Social-Web-2nd-Edition

http://nbviewer.jupyter.org/url/norvig.com/ipython/TSP.ipynb

http://ipython-books.github.io/featured-04/

https://github.com/tiagoantao/bioinf-python/blob/master/notebooks/Welcome.ipynb

http://nbviewer.jupyter.org/github/nealcaren/workshop_2014/tree/master/notebooks/

http://nbviewer.jupyter.org/gist/rpmuller/5920182

http://nbviewer.jupyter.org/github/gumption/Python_for_Data_Science/blob/master/Python_for_Data_Science_all.ipynb

http://nbviewer.jupyter.org/github/phelps-sg/python-bigdata/blob/master/src/main/ipynb/numerical-slides.ipynb

http://nbviewer.jupyter.org/github/twiecki/pymc3_talk/blob/master/bayesian_pymc3.ipynb

http://nbviewer.jupyter.org/github/andressotov/News-Categorization-MNB/blob/master/News%20Categorization%20MNB.ipynb

http://pyke.sourceforge.net/

http://nbviewer.jupyter.org/url/www.inp.nsk.su/%7Egrozin/python/sympy.ipynb

http://www.clips.ua.ac.be/pattern

https://github.com/proycon/pynlpl

https://github.com/machinalis/quepy

http://textblob.readthedocs.io/en/dev/

https://pypi.python.org/pypi/bllipparser/

http://konlpy.org/en/v0.4.4/

https://github.com/pprett/nut

https://github.com/duanhongyi/genius

https://github.com/fangpenlin/loso

https://github.com/isnowfy/snownlp

https://github.com/fxsjy/jieba#jieba-1

https://github.com/machinalis/yalign

https://github.com/EducationalTestingService/python-zpar

https://pypi.python.org/pypi/clearnlp-converter/

https://github.com/perone/Pyevolve

https://github.com/breze-no-salt/breze

https://github.com/pymc-devs/pymc

http://seaborn.pydata.org/

https://github.com/jaredthecoder/BioPy

https://github.com/kevincobain2000/jProcessing

https://github.com/zygmuntz/kaggle-blackbox

https://github.com/wiseodd/generative-models

http://www.abigailsee.com/2017/04/16/taming-rnns-for-better-summarization.html

https://chatbotslife.com/real-time-image-recognition-and-speech-5545f267f7b3

https://medium.com/becoming-human/a-news-analysis-neuralnet-learns-from-a-language-neuralnet-16646804fdeb

https://conference.scipy.org/scipy2013/tutorial_detail.php?id=109

https://www.analyticsvidhya.com/blog/2016/01/complete-tutorial-learn-data-science-python-scratch-2/

https://people.duke.edu/~ccc14/sta-663/ http://ipython-books.github.io/featured-07/

https://github.com/williamleif/socialsent

http://rylanschaeffer.github.io/content/research/one_shot_learning_with_memory_augmented_nn/main.html

https://bibinlp.umiacs.umd.edu/sharedtask.html

https://github.com/nightrome/really-awesome-gan

http://www.abigailsee.com/2017/04/16/taming-rnns-for-better-summarization.html

https://github.com/nyu-dl/SearchQA

https://altair-viz.github.io/

https://junyanz.github.io/CycleGAN/

https://github.com/phillipi/pix2pix

https://github.com/miguelgfierro/sciblog_support/blob/master/Intro_to_Machine_Learning_API/Intro_to_Cloud_ML_with_Flask_and_CNTK.ipynb

http://textbookqa.org/ http://people.cs.georgetown.edu/cosc672/s17/

https://matrices.io/deep-neural-network-from-scratch/

http://introtodeeplearning.com/

https://github.com/nschneid/amr-hackathon/tree/master/src

https://github.com/yoonkim/lstm-char-cnn

http://www.kdnuggets.com/2016/04/top-10-ipython-nb-tutorials.html

http://usblogs.pwc.com/emerging-technology/wp-content/uploads/2017/04/PwC_Next-in-Tech_Infographic_Machine-learning-methods_2017.pdf

http://www.datadependence.com/2016/04/scientific-python-matplotlib/

http://www.kdnuggets.com/2016/06/intro-scientific-python-matplotlib.html

http://dataaspirant.com/2017/03/02/how-logistic-regression-model-works/

http://web.stanford.edu/class/cs224n/reports.html

http://cs224d.stanford.edu/reports_2016.html

http://cs224d.stanford.edu/reports.html

http://opennmt.net/

https://medium.com/becoming-human/q-a-system-deep-learning-2-2-c0ad60800e3

https://medium.com/@kashyapraval/qna-system-deep-learning-1-2-4aa20c017042

https://medium.com/becoming-human/an-introduction-to-tensorflow-f4f31e3ea1c0

https://github.com/Nuelsian/neuro-lab

http://www.kdnuggets.com/2016/06/intro-scientific-python-numpy.html

https://github.com/soulmachine/machine-learning-cheat-sheet

https://medium.com/becoming-human/back-propagation-is-very-simple-who-made-it-complicated-97b794c97e5c

https://www.youtube.com/watch?v=iz-TZOEKXzA

https://caffe2.ai/

https://venturebeat.com/2017/04/18/facebook-open-sources-caffe2-a-new-deep-learning-framework/

https://github.com/dnouri/nolearn

https://github.com/nitishsrivastava/deepnet

https://github.com/sdemyanov/ConvNet

http://cs.stanford.edu/people/karpathy/convnetjs/

https://rare-technologies.com/rrp-3-andy-mueller-on-scikit-learn-and-open-source/

https://www.ft.com/content/048f418c-2487-11e7-a34a-538b4cb30025

https://minhlab.wordpress.com/2016/01/12/reproducing-chen-manning-2014/

https://github.com/songrotek/Deep-Learning-Papers-Reading-Roadmap

http://adventuresinmachinelearning.com/convolutional-neural-networks-tutorial-tensorflow/

http://adventuresinmachinelearning.com/python-tensorflow-tutorial/

http://cs.nyu.edu/~kcho/DMQA/

https://github.com/ageron/handson-ml https://github.com/jiweil/Neural-Dialogue-Generation

http://adventuresinmachinelearning.com/neural-networks-tutorial/

http://adventuresinmachinelearning.com/stochastic-gradient-descent/

https://github.com/dennybritz/deeplearning-papernotes

https://deephunt.in/the-gan-zoo-79597dc8c347

https://blogs.microsoft.com/next/2016/01/25/microsoft-releases-cntk-its-open-source-deep-learning-toolkit-on-github/#sm.00007zzbh8k47cwtpgr2ka9dl1r2c

https://opensource.googleblog.com/2016/10/introducing-open-images-dataset.html

http://tkipf.github.io/graph-convolutional-networks/

https://github.com/tkipf/gcn

http://www.datasciencecentral.com/profiles/blogs/a-primer-in-adversarial-machine-learning-the-next-advance-in-ai

https://github.com/dennybritz/reinforcement-learning https://github.com/ritchieng/the-incredible-pytorch

https://github.com/aymericdamien/TopDeepLearning?utm_campaign=Revue%20newsletter&utm_medium=Newsletter&utm_source=revue

https://github.com/lisa-lab/DeepLearningTutorials

https://adeshpande3.github.io/adeshpande3.github.io/Deep-Learning-Research-Review-Week-1-Generative-Adversarial-Nets

http://mogren.one/blog/2016/08/08/trends-in-neural-machine-translation.html?utm_campaign=Revue%20newsletter&utm_medium=Newsletter&utm_source=revue

https://medium.com/@camrongodbout/recurrent-neural-networks-for-beginners-7aca4e933b82

https://github.com/karpathy/neuraltalk2

https://github.com/tflearn/tflearn

https://github.com/karpathy/neuraltalk

https://github.com/karpathy/char-rnn

https://github.com/aymericdamien/TensorFlow-Examples

https://github.com/alexjc/neural-doodle

https://github.com/ryankiros/neural-storyteller

https://github.com/Newmu/Theano-Tutorials

https://github.com/jcjohnson/neural-style

https://github.com/google/deepdream

https://github.com/Rochester-NRT/RocAlphaGo

https://github.com/Rochester-NRT/RocAlphaGo/wiki/01.-Home

https://github.com/awentzonline/image-analogies

https://github.com/lisa-lab/pylearn2

https://github.com/aigamedev/scikit-neuralnetwork

https://github.com/Newmu/Theano-Tutorials

https://github.com/Newmu/dcgan_code

https://rubenfiszel.github.io/posts/rl4j/2016-08-24-Reinforcement-Learning-and-DQN.html

http://www.wildml.com/2016/08/rnns-in-tensorflow-a-practical-guide-and-undocumented-features/

https://medium.com/%40ageitgey/machine-learning-is-fun-part-5-language-translation-with-deep-learning-and-the-magic-of-sequences-2ace0acca0aa

http://www.wildml.com/2016/10/learning-reinforcement-learning/

http://blog.revolutionanalytics.com/2016/08/deep-learning-part-2.html

http://www.wildml.com/2016/08/rnns-in-tensorflow-a-practical-guide-and-undocumented-features/

https://github.com/titu1994/Neural-Style-Transfer

https://research.fb.com/the-long-game-towards-understanding-dialog/

http://www.kdnuggets.com/2015/03/deep-learning-text-understanding-from-scratch.html

https://medium.com/becoming-human/back-propagation-is-very-simple-who-made-it-complicated-97b794c97e5c

http://distill.pub/2016/augmented-rnns/

https://github.com/danijar/mindpark

https://github.com/Babylonpartners/fastText_multilingual

https://research.googleblog.com/2016/09/show-and-tell-image-captioning-open.html

https://pypi.python.org/pypi/ConceptNet/5.5.0

https://blog.metaflow.fr/tensorflow-a-proposal-of-good-practices-for-files-folders-and-models-architecture-f23171501ae3

https://www.youtube.com/watch?v=9qZMyfVcq30

Facebook AI Director: The Next Frontier in AI, Unsupervised Learning http://p.migdal.pl/2017/04/30/teaching-deep-learning.html

http://gainfromhere.com/deep-learning-recurrent-neural-networks-in-python/

http://www.datasciencecentral.com/profiles/blogs/handling-imbalanced-data-sets-in-supervised-learning-using-family

http://sebastianraschka.com/Articles/2014_python_lda.html

https://www.youtube.com/watch?v=wuo4JdG3SvU&list=PL9Hr9sNUjfsmEu1ZniY0XpHSzl5uihcXZ&index=1

https://www.quora.com/How-do-I-learn-Machine-Learning-in-10-days

https://www.youtube.com/watch?v=nbJ-2G2GXL0

PyTorch in 5 minutes http://students.brown.edu/seeing-theory/

http://www.jeannicholashould.com/tidy-data-in-python.html

https://medium.com/@ageitgey/try-deep-learning-in-python-now-with-a-fully-pre-configured-vm-1d97d4c3e9b

https://chatbotslife.com/training-mxnet-part-1-mnist-6f0dc4210c62

https://medium.com/becoming-human/training-mxnet-part-2-cifar-10-c7b0b729c33c

https://medium.com/@julsimon/training-mxnet-part-3-cifar-10-redux-ecab17346aa0

https://www.kaggle.com/google-nlu/text-normalization

https://medium.com/becoming-human/making-a-simple-neural-network-2ea1de81ec20

https://github.com/abisee/pointer-generator

https://github.com/tensorflow/models/tree/master/textsum

http://www.kdnuggets.com/2017/05/guerrilla-guide-machine-learning-python.html

http://www.kdnuggets.com/2017/04/build-recurrent-neural-network-tensorflow.html

https://www.youtube.com/watch?v=wuo4JdG3SvU

TensorFlow Tutorial

01 Simple Linear Model

https://medium.com/becoming-human/neural-network-xor-application-and-fundamentals-6b1d539941ed

https://www.youtube.com/watch?v=WHQS35IT75c

Stanford AI Director: How AI & Computer Vision will Drive our Future https://github.com/Vaibhavs10/10_days_of_deep_learning

http://hamelg.blogspot.com/2015/11/python-for-data-analysis-part-24.html

https://blog.waya.ai/introduction-to-gans-a-boxing-match-b-w-neural-nets-b4e5319cc935

参考资料

• 基于http://www.cs.yale.edu/homes/radev/dlnlp2017.pdf资源整理修正