└── README.md /README.md: -------------------------------------------------------------------------------- 1 | # Deep-tutorials 2 | 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. 3 | 4 | ## Table of Contents 5 | - [Unsupervised Learning](#unsupervised-learning) 6 | - [Reinforcement Learning](#reinforcement-learning) 7 | - [Computer Vision](#computer-vision) 8 | - [NLP](#nlp) 9 | - [Algorithms and Mathematics](#algorithms-and-mathematics) 10 | - [Implementations](#implementations) 11 | - [MISC](#misc) 12 | 13 | ## Unsupervised Learning 14 | * [Introduction to Generative Adversarial Networks](http://www.iangoodfellow.com/slides/2016-12-9-gans.pdf) 15 | * Ian Goodfellow, NIPS 2016 16 | * [Adversarial Approaches to Bayesian Learning and Bayesian Approaches to Adversarial Robustness](http://www.iangoodfellow.com/slides/2016-12-10-bayes.pdf) 17 | * Ian Goodfellow, NIPS 2016 18 | * [Adversarial Examples and Adversarial Training](http://www.iangoodfellow.com/slides/2016-12-9-AT.pdf) 19 | * Ian Goodfellow, NIPS 2016 20 | * [Energy-based GANs & Other Adversarial Things](https://drive.google.com/file/d/0BxKBnD5y2M8NbzBUbXRwUDBZOVU/view) 21 | * Yann LeCun, NIPS 2016 22 | * [Predictive Learning](https://drive.google.com/file/d/0BxKBnD5y2M8NREZod0tVdW5FLTQ/view) 23 | * Yann LeCun, NIPS 2016 24 | * [Variational Inference: Foundations and Modern Methods](http://www.cs.columbia.edu/~blei/talks/2016_NIPS_VI_tutorial.pdf) 25 | * David Blei, Rajesh Ranganath, Shakir Mohamed, NIPS 2016 26 | * [Learning Deep Generative Models](https://drive.google.com/open?id=0B_wzP_JlVFcKYXZKTkJWUWE2NjA) 27 | * Ruslan Salakhutdinov, Deep Learning Summer School 2016 28 | * [Building Machines that Imagine and Reason: Principles and Applications of Deep Generative Models](https://drive.google.com/open?id=0B_wzP_JlVFcKMnFMNnAtYTVkV28) 29 | * Shakir Mohamed, Deep Learning Summer School 2016 30 | 31 | 32 | ## Reinforcement Learning 33 | * [Deep Reinforcement Learning through Policy Optimization](https://people.eecs.berkeley.edu/~pabbeel/nips-tutorial-policy-optimization-Schulman-Abbeel.pdf) 34 | * Pieter Abbeel, John Schulman, NIPS 2016 35 | * [The Nuts and Bolts of Deep RL Research](http://rll.berkeley.edu/deeprlcourse/docs/nuts-and-bolts.pdf) 36 | * John Schulman, NIPS 2016 37 | * [Tutorial: Deep Reinforcement Learning](http://icml.cc/2016/tutorials/deep_rl_tutorial.pdf) 38 | * David Silver, ICML 2016 39 | * [AlphaGo](http://icml.cc/2016/tutorials/AlphaGo-tutorial-slides.pdf) 40 | * David Silver, ICML 2016 41 | * [Introduction to Reinforcement Learning](https://drive.google.com/open?id=0B_wzP_JlVFcKdDg4Yy1XQTBZLUhGTG5tT29reXdYcXdES1lv) 42 | * Joelle Pineau, Deep Learning Summer School 2016 43 | * [Deep Reinforcement Learning](https://drive.google.com/open?id=0B_wzP_JlVFcKS2dDWUZqTTZGalU) 44 | * Pieter Abbeel, Deep Learning Summer School 2016 45 | 46 | 47 | ## Computer Vision 48 | * [Hierarchical Object Detection with Deep Reinforcement Learning](http://www.slideshare.net/xavigiro/hierarchical-object-detection-with-deep-reinforcement-learning) 49 | * Miriam Bellver, Xavier Giroi Niento, Ferran Marques, Jordi Torres, NIPS 2016 50 | * [Deep Learning for Computer Vision](https://tensorflowkorea.files.wordpress.com/2016/09/bay-area-deep-learning-school-presentation.pdf) 51 | * Andrej Karpathy, Bay Area DL School 2016 52 | * [Deep Residual Networks: Deep Learning Gets Way Deeper](http://icml.cc/2016/tutorials/icml2016_tutorial_deep_residual_networks_kaiminghe.pdf) 53 | * Kaiming He, ICML 2016 54 | * [Convolutional Neural Networks and Computer Vision](https://drive.google.com/file/d/0B_wzP_JlVFcKRkhra0dzUEdKLWc/view?usp=sharing) 55 | * Rob Fergus, Deep Learning Summer School 2016 56 | 57 | 58 | ## NLP 59 | * [Learning Program Representations: Symbols to Vectors to Semantics](http://homepages.inf.ed.ac.uk/csutton/talks/nampi2016-talk-sutton/nampi-sutton-2016.pdf) 60 | * Charles Sutton, NIPS 2016 61 | * [Memory Networks for Language Understanding](http://www.thespermwhale.com/jaseweston/icml2016/icml2016-memnn-tutorial.pdf) 62 | * Jason Weston, ICML 2016 63 | * [Recurrent Neural Networks](https://drive.google.com/file/d/0B_wzP_JlVFcKdlhTUG9KMTVjLVNhMlJKYnJybC1BcDJEVngw/view?usp=sharing) 64 | * Yoshua Bengio, Deep Learning Summer School 2016 65 | * [Reasoning, Attention and Memory](https://drive.google.com/open?id=0B_wzP_JlVFcKbHdpYVdZMjg3eTBQd2F1OG9QZlVhOGJoX0dz) 66 | * Sumit Chopra, Deep Learning Summer School 2016 67 | * [Deep Natural Language Understanding](https://drive.google.com/open?id=0B_wzP_JlVFcKc0xrMjRhRU9DN2JRQlB0TjdkdmpLZ0FSaTFZ) 68 | * Kyunghyun Cho, Deep Learning Summer School 2016 69 | * [Beyond Seq2Seq with Augmented RNNs](https://drive.google.com/open?id=0B_wzP_JlVFcKYTFaTVFJN18tbmtkX2V0WEEtWXVSdDV4UHVZ) 70 | * Edward Grefenstette, Deep Learning Summer School 2016 71 | 72 | 73 | ## Algorithms and Mathematics 74 | * [Towards Biologically Plausible Deep Learning](http://www.iro.umontreal.ca/~bengioy/talks/Brains+Bits-NIPS2016Workshop.pptx.pdf) 75 | * Yoshua Bengio, NIPS 2016 76 | * [Higher-order Factorization Machines](http://www.mblondel.org/talks/mblondel-stair-2016-09.pdf) 77 | * Mathieu Blondel, NIPS 2016 78 | * [Nuts and Bolts of Building AI Applications Using Deep Learning](https://www.dropbox.com/s/dyjdq1prjbs8pmc/NIPS2016%20-%20Pages%202-6%20(1).pdf) 79 | * Andrew Ng, NIPS 2016 80 | * [Joint Causal Inference on Observational and Experimental Datasets](http://www.slideshare.net/SaraMagliacane/talk-joint-causal-inference-on-observational-and-experimental-data-nips-2016-what-if-workshop-poster) 81 | * Sara Magliacane, Tom Claassen, Joris M. Mooij, NIPS 2016 82 | * [Foundations of Deep Learning](https://tensorflowkorea.files.wordpress.com/2016/09/hugo_dlss.pdf) 83 | * Hugo Larochelle, Bay Area DL School 2016 84 | * [Recent Advances in Non-Convex Optimization](http://newport.eecs.uci.edu/anandkumar/slides/icml2016-tutorial.pdf) 85 | * Anima Anandkumar, ICML 2016 86 | * Stochastic Gradient Methods for Large-Scale Machine Learning [[part1]](http://icml.cc/2016/tutorials/part-1.pdf)[[part2]](http://icml.cc/2016/tutorials/part-2.pdf)[[part3]](http://icml.cc/2016/tutorials/part-3.pdf) 87 | * Leon Buttou, Frank E. Curtis, Jorge Nocedal, ICML 2016 88 | * Online Convex Optimization, A Game-Theoretic Approach to Learning [[part1]](http://www.cs.princeton.edu/~ehazan/tutorial/OCO-tutorial-part1.pdf)[[part2]](http://www.cs.princeton.edu/~ehazan/tutorial/OCO-tutorial-part2.pdf) 89 | * Elad Hazan, Satyen Kale, ICML 2016 90 | * [Rigorous Data Dredging: Theory and Tools for Adaptive Data Analysis](http://www.cis.upenn.edu/~aaroth/Papers/icmltutorial.pptx) 91 | * Moritz Hardt, Aaron Roth, ICML 2016 92 | * Graph Sketching, Streaming, and Space-Efficient Optimization [[part1]](http://icml.cc/2016/tutorials/16-icml-part1.pdf)[[part2]](http://icml.cc/2016/tutorials/16-icml-part2.pdf) 93 | * Sudipto Guha, Andrew McGregor, ICML 2016 94 | * [Causal Inference for Observational Studies](http://www.cs.nyu.edu/~shalit/slides.pdf) 95 | * David Sontag, Uri Shalit, ICML 2016 96 | * [Introduction to Machine Learning](https://drive.google.com/file/d/0B_wzP_JlVFcKTHF1RmxSbmJSb200WUVpLVFNX21nYkdjLWJv/view?usp=sharing) 97 | * Doina Precup, Deep Learning Summer School 2016 98 | * [Neural Networks](https://drive.google.com/file/d/0B_wzP_JlVFcKRDNuOXF2WnpLSzg/view?usp=sharing) 99 | * Hugo Larochelle, Deep Learning Summer School 2016 100 | 101 | 102 | ## Implementations 103 | * Theano Tutorial [[BA]](https://github.com/lamblin/bayareadlschool)[[DLSS]](https://github.com/mila-udem/summerschool2016/raw/master/theano/course/intro_theano.pdf) 104 | * Pascal Lamblin, Bay Area DL School, Deep Learning Summer School, 2016 105 | * [TensorFlow Tutorial](https://github.com/sherrym/tf-tutorial/raw/master/DeepLearningSchool2016.pdf) 106 | * Sherry Moore, Bay Area DL School 2016 107 | * [Introduction to Torch](https://drive.google.com/file/d/0B_wzP_JlVFcKWndJTEk3a2NDX0U/view?usp=sharing) 108 | * Alex Wiltschko, Deep Learning Summer School 2016 109 | * [Large Scale Deep Learning with TensorFlow](https://drive.google.com/open?id=0B_wzP_JlVFcKS2lydm5JdV9kMk1yUENoalA5TG5PV0lqWS1v) 110 | * Jeff Dean, Deep Learning Summer School 2016 111 | * [GPU Programming for Deep Learning](https://drive.google.com/open?id=0B_wzP_JlVFcKMXhjS21wVG92b0Npem5reUlUeThSZG1oV2U4) 112 | * Julie Bernauer, Deep Learning Summer School 2016 113 | 114 | 115 | ## MISC 116 | * [Crowdsourcing: Beyond Label Generation](http://www.jennwv.com/projects/crowdtutorial/crowdslides.pdf) 117 | * Jenn Wortman Vaughan, NIPS 2016 118 | * [Machine Learning & Likelihood Free Inference in Particle Physics](https://figshare.com/articles/NIPS_2016_Keynote_Machine_Learning_Likelihood_Free_Inference_in_Particle_Physics/4291565) 119 | * Kyle Cranmer, NIPS 2016 120 | * [FusionNet: 3D Object Classification Using Multiple Data Representations](http://matroid.com/papers/fusionnet_slides.pdf) 121 | * Vishakh Hedge, Reza Zadeh, NIPS 2016 122 | * [Beyond Inspiration: Five Lessons from Biology on Building Intelligent Machines](https://drive.google.com/open?id=0B_wzP_JlVFcKMUF4aUJNS3F5Tm1pUzhKTEZQV25nYjY1MXVj) 123 | * Bruno Olshausen, Deep Learning Summer School 2016 124 | * [Theoretical Neuroscience and Deep Learning Theory](https://drive.google.com/open?id=0B_wzP_JlVFcKeVRfVVNKX0NMelE) 125 | * Surya Ganguli, Deep Learning Summer School 2016 126 | --------------------------------------------------------------------------------