Deep-tutorials

A curated list of tutorial slides from conferences including NIPS, ICLR, ICML, and more. Also resources from Deep Learning Summer School would be included. Online lectures and journals are all informative, however these tutorials are also much valuable. Reading all state-of-the-art papers and news of machine learning is difficult. With these tutorials, I can grasp ideas from papers more easily.

Table of Contents

• Unsupervised Learning
• Reinforcement Learning
• Computer Vision
• NLP
• Algorithms and Mathematics
• Implementations
• MISC

Unsupervised Learning

• Introduction to Generative Adversarial Networks
  • Ian Goodfellow, NIPS 2016
• Adversarial Approaches to Bayesian Learning and Bayesian Approaches to Adversarial Robustness
  • Ian Goodfellow, NIPS 2016
• Adversarial Examples and Adversarial Training
  • Ian Goodfellow, NIPS 2016
• Energy-based GANs & Other Adversarial Things
  • Yann LeCun, NIPS 2016
• Predictive Learning
  • Yann LeCun, NIPS 2016
• Variational Inference: Foundations and Modern Methods
  • David Blei, Rajesh Ranganath, Shakir Mohamed, NIPS 2016
• Learning Deep Generative Models
  • Ruslan Salakhutdinov, Deep Learning Summer School 2016
• Building Machines that Imagine and Reason: Principles and Applications of Deep Generative Models
  • Shakir Mohamed, Deep Learning Summer School 2016

Reinforcement Learning

• Deep Reinforcement Learning through Policy Optimization
  • Pieter Abbeel, John Schulman, NIPS 2016
• The Nuts and Bolts of Deep RL Research
  • John Schulman, NIPS 2016
• Tutorial: Deep Reinforcement Learning
  • David Silver, ICML 2016
• AlphaGo
  • David Silver, ICML 2016
• Introduction to Reinforcement Learning
  • Joelle Pineau, Deep Learning Summer School 2016
• Deep Reinforcement Learning
  • Pieter Abbeel, Deep Learning Summer School 2016

Computer Vision

• Hierarchical Object Detection with Deep Reinforcement Learning
  • Miriam Bellver, Xavier Giroi Niento, Ferran Marques, Jordi Torres, NIPS 2016
• Deep Learning for Computer Vision
  • Andrej Karpathy, Bay Area DL School 2016
• Deep Residual Networks: Deep Learning Gets Way Deeper
  • Kaiming He, ICML 2016
• Convolutional Neural Networks and Computer Vision
  • Rob Fergus, Deep Learning Summer School 2016

NLP

• Learning Program Representations: Symbols to Vectors to Semantics
  • Charles Sutton, NIPS 2016
• Memory Networks for Language Understanding
  • Jason Weston, ICML 2016
• Recurrent Neural Networks
  • Yoshua Bengio, Deep Learning Summer School 2016
• Reasoning, Attention and Memory
  • Sumit Chopra, Deep Learning Summer School 2016
• Deep Natural Language Understanding
  • Kyunghyun Cho, Deep Learning Summer School 2016
• Beyond Seq2Seq with Augmented RNNs
  • Edward Grefenstette, Deep Learning Summer School 2016

Algorithms and Mathematics

• Towards Biologically Plausible Deep Learning
  • Yoshua Bengio, NIPS 2016
• Higher-order Factorization Machines
  • Mathieu Blondel, NIPS 2016
• Nuts and Bolts of Building AI Applications Using Deep Learning
  • Andrew Ng, NIPS 2016
• Joint Causal Inference on Observational and Experimental Datasets
  • Sara Magliacane, Tom Claassen, Joris M. Mooij, NIPS 2016
• Foundations of Deep Learning
  • Hugo Larochelle, Bay Area DL School 2016
• Recent Advances in Non-Convex Optimization
  • Anima Anandkumar, ICML 2016
• Stochastic Gradient Methods for Large-Scale Machine Learning [part1][part2][part3]
  • Leon Buttou, Frank E. Curtis, Jorge Nocedal, ICML 2016
• Online Convex Optimization, A Game-Theoretic Approach to Learning [part1][part2]
  • Elad Hazan, Satyen Kale, ICML 2016
• Rigorous Data Dredging: Theory and Tools for Adaptive Data Analysis
  • Moritz Hardt, Aaron Roth, ICML 2016
• Graph Sketching, Streaming, and Space-Efficient Optimization [part1][part2]
  • Sudipto Guha, Andrew McGregor, ICML 2016
• Causal Inference for Observational Studies
  • David Sontag, Uri Shalit, ICML 2016
• Introduction to Machine Learning
  • Doina Precup, Deep Learning Summer School 2016
• Neural Networks
  • Hugo Larochelle, Deep Learning Summer School 2016

Implementations

• Theano Tutorial [BA][DLSS]
  • Pascal Lamblin, Bay Area DL School, Deep Learning Summer School, 2016
• TensorFlow Tutorial
  • Sherry Moore, Bay Area DL School 2016
• Introduction to Torch
  • Alex Wiltschko, Deep Learning Summer School 2016
• Large Scale Deep Learning with TensorFlow
  • Jeff Dean, Deep Learning Summer School 2016
• GPU Programming for Deep Learning
  • Julie Bernauer, Deep Learning Summer School 2016

MISC

• Crowdsourcing: Beyond Label Generation
  • Jenn Wortman Vaughan, NIPS 2016
• Machine Learning & Likelihood Free Inference in Particle Physics
  • Kyle Cranmer, NIPS 2016
• FusionNet: 3D Object Classification Using Multiple Data Representations
  • Vishakh Hedge, Reza Zadeh, NIPS 2016
• Beyond Inspiration: Five Lessons from Biology on Building Intelligent Machines
  • Bruno Olshausen, Deep Learning Summer School 2016
• Theoretical Neuroscience and Deep Learning Theory
  • Surya Ganguli, Deep Learning Summer School 2016