Machine learning

Topics #

• Autoencoder
• Brain and deep learning
• Convolutional neural network
• Deep generative models
• Deep learning and graphs
• Differentiable neural computers
• Generative adversarial network
• Recurrent neural network
• Residual Learning
• word2vec

Courses #

• CS231n: Convolutional Neural Networks for Visual Recognition
  • Videos
• CS224n: Natural Language Processing with Deep Learning
  • Videos
• Coursera: deep learning specialization
• Microsoft Professional Program for Artificial Intelligence track
• Google: deep learning
• Hugo Larochelle: Neural networks
• Geoffrey Hinton Coursera videos
• Probabilistic Graphical Model by Daphne Koller

Books #

• http://www.deeplearningbook.org/

Tools #

• Evaluation of Deep Learning Toolkits
• TensorFlow
• Keras

Visualization #

• https://github.com/rewonc/pastalog

Talks and tutorials #

• A Tutorial on Deep Learning Part 1: Nonlinear Classifiers and The Backpropagation Algorithm by Quoc Le
  • Autoencoders, Convolutional Neural Networks and Recurrent Neural Networks
• Deep Learning Basics: Neural Networks, Backpropagation and Stochastic Gradient Descent
• ufldl stanford:Deep Learning Tutorials
• sjchoi86:Deep learning tutorials
• Unsupervised Feature Learning and Deep Learning by Andrew Ng
• Use Google's Word2Vec for movie reviews
• Using convolutional neural nets to detect facial keypoints tutorial
• Deep Learning: Doubly Easy and Doubly Powerful with GraphLab Create - GraphLab
• ICML2013 Deep learning tutorial by Yann LeCun and Marc'Aurelio Ranzato

Articles #

• http://fastml.com/deep-learning-made-easy/
• So You Wanna Try Deep Learning?
• Recommending music on Spotify with deep learning
• Inceptionism: Going Deeper into Neural Networks

References #

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

Review #

• Deep learning by Yann LeCun, Yoshua Bengio, and Geoffrey Hinton
  • Critique of Paper by "Deep Learning Conspiracy" (Nature 521 p 436 by Jürgen Schmidhuber

Augmenting small datasets with transformations #

• Some Improvements on Deep Convolutional Neural Network Based Image Classification

Noise #

• ADDING GRADIENT NOISE IMPROVES LEARNING FOR VERY DEEP NETWORKS - Thermostat

Adversarial examples #

• Deep Neural Networks are Easily Fooled: High Confidence Predictions for Unrecognizable Images
  • Youtube
• Universal adversarial perturbations

About baselines #

• Simple Baseline for Visual Question Answering

Natural language processing #

• ATTENTION AND MEMORY IN DEEP LEARNING AND NLP

Binarization #

• Deep neural networks are robust to weight binarization and other non-linear distortions

Others #

• http://blog.kaggle.com/2012/11/01/deep-learning-how-i-did-it-merck-1st-place-interview/
• Building High-level Features Using Large Scale Unsupervised Learning
• Deep Neural Networks for Acoustic Modeling in Speech Recognition
• ImageNet Classification with Deep Convolutional Neural Networks
• Large Scale Distributed Deep Networks
• How transferable are features in deep neural networks?
• Human-level control through deep reinforcement learning
  • AI masters 49 Atari 2600 games without instructions

Incoming Links #

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Related Scholarly Articles (ScholarlyArticle 1) #

• Paper/Gatys2015

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