Deep Learning - Curated Reading List

machine_learning
data_science
deeplearning

#1

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


#2

Excellent list Sohom! This article also contains some great links on deep learning