We're doing a biweekly study group about Deep Learning in Amsterdam: 1 evening (2-3h) every other week. It's for a group of people with strong CS + LA background, pref. some familiarity with machine learning and neural network basics.
| Session | Date | Theory | Coding |
|---|---|---|---|
| week 1 | 2016-09-18 | intro talk, NN&DL ch1 | ch1 coding assignments |
| week 2 | 2016-10-02 | NN&DL^ ch2 | webcam demo |
| week 3 | 2016-10-16 | NN&DL^ ch3 | sk-learn neural nets |
| week 4 | 2016-10-30 | NN&DL^ ch4 + ch5 | intro TensorFlow |
| week 5 | 2016-11-13 | NN&DL^ ch6 | TensorFlow (part 2) |
| week 6 | 2016-11-27 | discussion | TensorFlow (projects) |
| week 7 | 2016-12-11 | DL$ ch10, RNNs, papers | TensorFlow (projects) |
| week 8 | 2016-12-25 | (skip) | |
| week 9 | 2017-01-08 | DL$ ch11, papers | run existing projects |
| week 10 | 2017-01-22 | DL$ ch12 (apps), papers | Projects show-off, cocktails |
^ [book] Neural Networks and Deep Learning, by Michael Nielsen
$ [book] Deep Learning, by Goodfellow, Bengio and Courville (full reading notes)
syllabus, proposal: practical approach, but with deep understanding (not just trying github repos): book Nielsen (ML -> DL), then a course (either Google, creative DL, or (outdated) coursera) with corresponding homeworks. Extra: papers (DL classics, microsoft ebook, IBM watson, DeepMind), practical DL projects (TensorFlow, Theano, Torch, Keras.io), deep dreaming, GPU programming
- [book] by Michael Nielsen: http://neuralnetworksanddeeplearning.com/chap1.html
- [book] upcoming, view online: http://www.deeplearningbook.org
- [course] UvA, Max Welling: https://uvadlc.github.io/ in Torch -> also lists 'similar courses'
- [course] by Hinton (2012): https://www.coursera.org/course/neuralnets, supposedly outdated algorithms (in apr 2016)
- [course] Deep Learning (TensorFlow, by Google): https://www.udacity.com/course/deep-learning--ud730 (part of nanodegree ML)
- [course] Deep Learning & creative applications [2016]: https://www.kadenze.com/courses/creative-applications-of-deep-learning-with-tensorflow/info
- [blogpost] Demystifying Deep Reinforcement Learning [2015]: https://www.nervanasys.com/demystifying-deep-reinforcement-learning/
- [video lectures] RL Course by David Silver [2015]: https://www.youtube.com/watch?v=2pWv7GOvuf0
- [blogpost series] RL + DNNs in TensorFlow [2016], multiple posts: https://medium.com/emergent-future/simple-reinforcement-learning-with-tensorflow-part-0-q-learning-with-tables-and-neural-networks-d195264329d0#.967pe8962
- [blogpost] Differentiable neural computers [2016]: https://deepmind.com/blog/differentiable-neural-computers/ (With link to paper behind paywall)
- [blogpost] RNNs: The Unreasonable Effectiveness of Recurrent Neural Networks [2015] http://karpathy.github.io/2015/05/21/rnn-effectiveness/
- how it started: Hinton 2006: deep belief networks
- DeepMind & Google papers: Atari, AlphaGo, WaveNet), Building High-level Features Using Large Scale Unsupervised Learning ("Youtube paper"), Vincent Vanhoucke 2012-2015 ("Android papers")
- Watson (Building Watson, many other papers): various techniques combined
- Turing machine (2014 RNN python output predictor, 2014 Neural Turing Machines)
- more recent: ICCV 2015 CNN examples http://www.computervisionblog.com/2015/12/iccv-2015-twenty-one-hottest-research.html; more examples in these slide decks: SLAM & geometry, Depth vision & geometry, etc
- your favourite paper!
-
theano (Python) paper [4p] http://biglearn.org/2011/files/papers/biglearn2011_submission_18.pdf
-
torch (Lua; Facebook/FAIR contribs; used by Google) paper [5p] http://publications.idiap.ch/downloads/reports/2002/rr02-46.pdf
-
TensorFlow (python/c++) by Google http://www.tensorflow.org
-
Keras.io High-level Python library using theano and tensorflow, also CV
Images
- MNIST (60k hand-written digits; 28x28px) http://yann.lecun.com/exdb/mnist/
- CIFAR-10/CIFAR-100 (60k, 10/100 classes; 32x32pxRGB) http://www.cs.toronto.edu/%7Ekriz/cifar.html
- SVHN public Street View House Numbers (600k; box+label per digit) http://ufldl.stanford.edu/housenumbers/
- Image-net (16M, 20k classes) or subset ILSVRC (3M; 1000 classes) http://image-net.org/
- NYU depth v2 (fixed scenes) http://cs.nyu.edu/~silberman/datasets/nyu_depth_v2.html
- KITTI (data from car driving) http://www.cvlibs.net/datasets/kitti/ (have more code and datasets)
- more datasets: http://www.cvlibs.net/links.php?page=Main+page
Language
- Amazon Reviews (35M; 2015) https://snap.stanford.edu/data/web-Amazon.html
- Movielens (20M) http://grouplens.org/datasets/movielens/
- WordNet, per language http://www.certifiedchinesetranslation.com/openaccess/WordNet/
- Google Books n-grams (2TB of txt) https://storage.googleapis.com/books/ngrams/books/datasetsv2.html
- TIMIT acoustic corpus https://catalog.ldc.upenn.edu/ldc93s1
- NetFlix Price (recommender systems) http://academictorrents.com/details/9b13183dc4d60676b773c9e2cd6de5e5542cee9a
Other
- Kaggle datasets https://www.kaggle.com/datasets
- AWS datasets overview https://aws.amazon.com/datasets/
- EU open datasets https://data.europa.eu/euodp/en/data
- Wikipedia listing per data type https://en.wikipedia.org/wiki/List_of_datasets_for_machine_learning_research
- self-learning Mario or Atari games; homebrew: https://www.youtube.com/watch?v=qv6UVOQ0F44; DeepMind: https://www.youtube.com/watch?v=V1eYniJ0Rnk, paper https://arxiv.org/pdf/1312.5602v1.pdf