Deep Learning - Curated Reading List

@sohomg wrote:

I have created a list of deep learning resources. Please feel free to suggest more.
Here it is:

Topics

1. General Deep Learning (Fully connected nets)
2. Image Models [2D] (Convolutional Networks)
3. 1D Sequence Models
   Recurrent Neural Network (http://karpathy.github.io/2015/05/21/rnn-effectiveness/)
   Long Short Term Memory
   Attention models (https://machinelearningmastery.com/attention-long-short-term-memory-recurrent-neural-networks/)
   Gated Recurrent Units etc.
4. Other
   Unsupervised Learning,
   Deep Reinforcement Learning (http://karpathy.github.io/2016/05/31/rl/)
   Sparse coding
   Slow feature analysis etc.
5. AutoEncoders (http://www.deeplearningbook.org/contents/autoencoders.html)
6. Deep Belief Networks
7. Deep and restricted Boltzmann Machines (http://www.deeplearningbook.org/contents/generative_models.html)
8. Generative Adversarial Networks (http://www.deeplearningbook.org/contents/generative_models.html)
9. Deep Learning for NLP https://github.com/nyu-dl/NLP_DL_Lecture_Note/blob/master/lecture_note.pdf , http://u.cs.biu.ac.il/~yogo/nnlp.pdf
10. Capsule Network :

Book

1. deeplearningbook.org

Research papers

1. http://deeplearning.net/reading-list/

Videos

1. Oxford Deep Learning YouTube Playlist https://www.youtube.com/playlist?list=PLE6Wd9FR--EfW8dtjAuPoTuPcqmOV53Fu
2. Coursera Neural Networks for Machine Learning https://www.youtube.com/watch?v=cbeTc-Urqak&list=PLoRl3Ht4JOcdU872GhiYWf6jwrk_SNhz9
3. AV Videos Playlist https://www.analyticsvidhya.com/blog/2016/12/21-deep-learning-videos-tutorials-courses-on-youtube-from-2016/

University Resources

1. Berkeley CS 294: Deep Reinforcement Learning, Fall 2017 http://rll.berkeley.edu/deeprlcourse/
2. CS 294-131: Special Topics in Deep Learning https://berkeley-deep-learning.github.io/cs294-131-s17/
3. Satnford Deep Learning using Tensorflow http://web.stanford.edu/class/cs20si/syllabus.html
4. Oxford deep nlp : https://github.com/oxford-cs-deepnlp-2017/lectures
5. Stanford deep nlp Richard Socher: http://web.stanford.edu/class/cs224n/syllabus.html
6. Ali Godse: https://www.youtube.com/watch?v=XTWPyW2mTUg&list=PLehuLRPyt1HxTolYUWeyyIoxDabDmaOSB https://www.youtube.com/channel/UCKJNzy_GuvX3SAg3ipaGa8A/playlists
7. Advanced Deep NLP: http://www.kyunghyuncho.me/home/courses/ds-ga-3001-fall-2015
8. http://videolectures.net/deeplearning2016_montreal/
9. Theories of Deep Learning https://stats385.github.io/
10. http://course.fast.ai/lessons/lesson0.html
11. http://ufldl.stanford.edu/tutorial/
12. http://speech.ee.ntu.edu.tw/~tlkagk/courses_ML17_2.html

Code Links
Keras

1. http://machinelearningmastery.com/use-different-batch-sizes-training-predicting-python-keras/
2. http://machinelearningmastery.com/sequence-classification-lstm-recurrent-neural-networks-python-keras/
3. http://machinelearningmastery.com/text-generation-lstm-recurrent-neural-networks-python-keras/
4. http://machinelearningmastery.com/time-series-prediction-lstm-recurrent-neural-networks-python-keras/
5. http://machinelearningmastery.com/time-series-prediction-with-deep-learning-in-python-with-keras/
6. http://machinelearningmastery.com/understanding-stateful-lstm-recurrent-neural-networks-python-keras/
7. http://machinelearningmastery.com/time-series-forecasting-long-short-term-memory-network-python/
8. http://machinelearningmastery.com/sequence-classification-lstm-recurrent-neural-networks-python-keras/
9. http://machinelearningmastery.com/use-different-batch-sizes-training-predicting-python-keras/

Tensorflow

1. https://gist.github.com/martinwicke/6838c23abdc53e6bcda36ed9f40cff39
2. https://github.com/tensorflow/tensorflow/blob/r1.2/RELEASE.md
3. https://www.tensorflow.org/get_started/mnist/pros
4. https://www.tensorflow.org/get_started/mnist/beginners 5)https://github.com/tensorflow/tensorflow/blob/r1.1/tensorflow/examples/tutorials/mnist/mnist_softmax.py
5. https://www.tensorflow.org/get_started/tflearn
6. https://www.tensorflow.org/get_started/summaries_and_tensorboard
7. https://github.com/tensorflow/tensorflow/blob/r1.1/tensorflow/tensorboard/README.md
8. https://github.com/tensorflow/ecosystem
9. https://www.tensorflow.org/get_started/embedding_viz
10. https://www.tensorflow.org/get_started/graph_viz
11. https://www.tensorflow.org/get_started/get_started
12. https://github.com/random-forests/tensorflow-workshop/
13. https://www.tensorflow.org/deploy/distributed
14. https://www.tensorflow.org/api_docs/python/tf/train/ClusterSpec
    V.Imp16) https://github.com/nlintz/TensorFlow-Tutorials
15. https://github.com/PacktPublishing/Machine-Learning-with-TensorFlow-1.x

Reference: https://github.com/sohomghosh/DeepLearning_materials_codes_readingList/blob/master/reading_list.txt

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