Extensive Deep Learning course by MIT: http://introtodeeplearning.com/index.html
Videos and slides: http://introtodeeplearning.com/schedule.html
A week-long intro to deep learning methods with applications to machine translation, image recognition, game playing, image generation and more. A collaborative course incorporating labs in TensorFlow and peer brainstorming along with lectures. Course concludes with project proposals with feedback from staff and panel of industry sponsors.
Some links and resources:
- ML Crash Course: https://ml.berkeley.edu/blog/2016/11/06/tutorial-1/
- Cross entropy http://colah.github.io/posts/2015-09-Visual-Information/
- Python tutorial: http://www.learnpython.org/
- Python/Numpy tutorial: http://cs231n.github.io/python-numpy-tutorial/
- Reference for math/probability: chapter 2, 3 and 4 of http://www.deeplearningbook.org/
- Tensorflow Crash Course: http://nicklocascio.com/tensorflow-crash-course
- Intro-level deep learning book: http://neuralnetworksanddeeplearning.com/
- Advanced deep learning book: http://www.deeplearningbook.org/
- Lots of links to topics: http://yerevann.com/a-guide-to-deep-learning/
- Deep Learning/TensorFlow Udacity Course: https://www.udacity.com/course/deep-learning–ud730
- Yann LeCun’s ‘Efficient Backpropagation’ (goes through a lot of basics on how/why to train networks a certain way, including some theory behind it): yann.lecun.com/exdb/publis/pdf/lecun-98b.pdf
- Guide to Papers: https://github.com/songrotek/Deep-Learning-Papers-Reading-Roadmap
- Curating arXiv: arxiv-sanity.com
- Summaries of Papers: http://www.shortscience.org/
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- Attention & Memory: http://distill.pub/2016/augmented-rnns/
- Stanford CS 224 slides: http://cs224d.stanford.edu/syllabus.html
- LSTMs, vanishing gradient: http://harinisuresh.com/2016/10/09/lstms/
- NIPS: https://nips.cc/Conferences/2016/AcceptedPapers
- ICLR: https://openreview.net/group?id=ICLR.cc/2017/conference
- ICML: http://icml.cc/2016/?page_id=1649
- KDD: http://www.kdd.org/kdd2016/program/accepted-papersPapers from Conferences:
- Deep Learning Newsletter: http://www.wildml.com/newsletter/
- ML subreddit: https://www.reddit.com/r/MachineLearning/
- Andrej Karpathy’s blog: http://karpathy.github.io/
- Chris Olah’s blog: http://colah.github.io/
- Vanishing Gradient: http://neuralnetworksanddeeplearning.com/chap5.html#the_vanishing_gradient_problem
| Session | Part 1 | Part 2 | Lab | Additional Materials |
| 1 | State of Deep Learning in 2017 Slides –Video | Deep Sequence Modelling Slides – Video | Intro to TensorFlow I: Computation Graphs and MLPs Code | Coming soon |
| 2 | Deep Computer Vision Slides | Deep Generative Models Slides | Intro to TensorFlow II: Language Modeling with LSTMs Code | Coming soon |
| 3 | Multimodal Deep Learning Slides – Video | Deep Reinforcement Learning Slides – Video | Work time for paper reviews/project proposals | Coming soon |
| 4 | Guest Lectures: Google Brain, NVIDIA | Guest Lectures: IBM Watson, Amazon Alexa | Work time for paper reviews/project proposals + Sponsor Booths | Coming soon |
| 5 | Project Proposal Presentations | Project Proposal Presentations | Proposal Judging | Post-Class Reception |