COURSES AND BOOKS IN MACHINE LEARNING

FULL COURSES:

• CNNs for image recognition [Stanford] http://cs231n.github.io/

• Deep Learning for NLP [Stanford] http://cs224d.stanford.edu/

• NLP [Coursera] https://class.coursera.org/nlangp-001/lecture

• Neural Networks [Coursera] https://www.coursera.org/course/neuralnets

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

• Quantitive Finance & Machine Learning http://www.quantitativemacro.com/

• Deep learning for perception https://computing.ece.vt.edu/~f15ece6504/

TUTORIALS/BOOKS:

• Bayesian Methods and Probabilistic programming https://camdavidsonpilon.github.io/Probabilistic-Programming-and-Bayesian-Methods-for-Hackers/

• Small neural networks book http://neuralnetworksanddeeplearning.com/

• Bengio deep learning book http://www.deeplearningbook.org/

• UFLDL Tutorial http://ufldl.stanford.edu/tutorial/

• Brains, Minds and Machines Summer Course 2015 https://www.youtube.com/playlist?list=PLyGKBDfnk-iB_rPiS0BbSHefK1HJMrPK_

• Neural networks with Theano and Lasagne https://github.com/ebenolson/pydata2015

• http://deeplearning.net/tutorial/

COURSES AND BOOKS IN COMPUTER SCIENCE

• Functional Programming: https://github.com/MostlyAdequate/mostly-adequate-guide

• Probabilistic Programming: https://bitbucket.org/probprog/mlss2015

• Computational Investing [Coursera] https://www.coursera.org/learn/computational-investing

• Product Management [Coursera] https://www.coursera.org/specializations/product-management

• Tensor Algebra https://habrahabr.ru/post/261421/

• Physics http://theoreticalminimum.com/courses/classical-mechanics/2011/fall

POTENTIALLY USEFUL CODE

• https://github.com/cmusatyalab/openface

• http://www.robots.ox.ac.uk/~vgg/software/vgg_face/

• https://github.com/carpedm20/awesome-torch

• https://github.com/imatge-upc/saliency-2016-cvpr

• http://pjreddie.com/darknet/yolo/

• https://github.com/smichalowski/google_inception_v3_for_caffe

• https://pystruct.github.io/

• http://cnnlocalization.csail.mit.edu/

• https://github.com/emansim/text2image

• https://github.com/philkr/magic_init

• https://github.com/raghakot/keras-resnet

ARTICLES/VIDEOS TO READ/WATCH AND PRACTICE

CNN

• http://www.neutronest.moe/2015-11-15-LSTM-survey.html

• http://scs.ryerson.ca/~aharley/neural-networks/

• http://felixlaumon.github.io/2015/01/08/kaggle-right-whale.html

• http://blog.christianperone.com/2016/01/convolutional-hypercolumns-in-python/

• http://josephpcohen.com/w/visualizing-cnn-architectures-side-by-side-with-mxnet/

• http://blog.heuritech.com/2016/01/20/attention-mechanism/

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

• http://torch.ch/blog/2016/02/04/resnets.html

• https://medium.com/@harvitronix/using-reinforcement-learning-in-python-to-teach-a-virtual-car-to-avoid-obstacles-6e782cc7d4c6#.4zvcl34d3>

• http://engineering.flipboard.com/2015/05/scaling-convnets/

• http://engineering.curalate.com/2016/01/20/emojinet.html

• http://blog.otoro.net/2016/04/01/generating-large-images-from-latent-vectors/

• http://www.analyticsvidhya.com/blog/2016/04/deep-learning-computer-vision-introduction-convolution-neural-networks/

• https://habrahabr.ru/post/278425/

RNN + NLP

• http://www.wildml.com/2016/01/attention-and-memory-in-deep-learning-and-nlp/

• http://cs.stanford.edu/~quocle/tutorial2.pdf

• http://simaaron.github.io/Estimating-rainfall-from-weather-radar-readings-using-recurrent-neural-networks/

• https://medium.com/jim-fleming/implementing-lstm-a-search-space-odyssey-7d50c3bacf93#.togi3rtez

• https://docs.google.com/presentation/d/19QDuPmxB9RzQWKXp_t3yqxCvMBSMaOQk19KNZqUUgYQ/edit#slide=id.g76126f83b_0_431

• http://www.wildml.com/2015/11/understanding-convolutional-neural-networks-for-nlp/

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

• https://devblogs.nvidia.com/parallelforall/deep-learning-nutshell-sequence-learning/

• https://github.com/EderSantana/seya/blob/master/examples/NTM.ipynb

• https://nofreehunch.wordpress.com/2015/08/30/google-word2vec-tutorial-part-1/

PROBABILITY

• http://alexanderetz.com/2015/11/01/evidence-vs-conclusions/

• https://habrahabr.ru/post/244625/

CLUSTERING

• http://brandonrose.org/clustering

REINFORCEMENT LEARNING

• https://github.com/maxpumperla/betago

• http://blog.acolyer.org/2016/03/02/graying-the-black-box-understanding-dqns/

OTHER:

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

• http://www.itechflare.com/main/complete-guide-setup-tensorflow-windows/

BLOGS TO READ

• http://www.wildml.com/

• http://colah.github.io/

• http://avisingh599.github.io/blog/

• http://www.offconvex.org/

• http://www.erogol.com/

• http://ailev.livejournal.com/

SCIENTIFIC PAPERS TO READ

• Derivatives for convolutions: https://jianfengwang.files.wordpress.com/2015/07/forwardandbackwardpropagationofconvolutionallayer.pdf

• SqueezeNet: http://arxiv.org/pdf/1602.07360v2.pdf

• http://www.wisdom.weizmann.ac.il/~dannyh/Mircs/mircs.html

• Deep Spiking Networks: http://arxiv.org/pdf/1602.08323v1.pdf

• Riemannian Neural Networks: http://arxiv.org/pdf/1602.08007v1.pdf

• Deep Residual Learning: http://arxiv.org/pdf/1512.03385v1.pdf

• GENERATIVE ADVERSARIAL NETWORKS: http://arxiv.org/pdf/1511.06434v1.pdf

• http://gitxiv.com/posts/j9FtQgmHAQD8qceub/a-neural-conversational-model

• Generating Images from Captions with Attention: http://arxiv.org/abs/1511.02793

• Parsing and Sentence Understanding: http://www.foldl.me/uploads/papers/acl2016-spinn.pdf

• Semantic Object Parsing with Graph LSTM: http://arxiv.org/pdf/1603.07063v1.pdf

• Explaining the Predictions of Any Classifier: http://arxiv.org/pdf/1602.04938v1.pdf

• Regularizing CNN on the Loss Layer: http://research.microsoft.com/en-us/um/people/jingdw/pubs/cvpr16-disturblabel.pdf

• Identity Mappings in Deep Residual Networks: http://arxiv.org/pdf/1603.05027v2.pdf

• http://gitxiv.com/posts/jJxKbNKBPDE8cbcgm/learning-deep-features-for-discriminative-localizationр

Tags: machinelearning, deeplearing