Deep-Learning-for-NLP-Resources

List of resources to get started with Deep Learning for NLP. (Updated incrementally)

1. https://www.youtube.com/playlist?list=PL6Xpj9I5qXYEcOhn7TqghAJ6NAPrNmUBH : This lecture series has very good introduction to Neural Network and Deep Learning.

2. https://www.coursera.org/course/neuralnets : This lecture series is from Geof Hinton. The concepts explained are bit abstract, concepts are hard to understand in first go. Generally people recommend these lectures as starting point but I am skeptical about it. I would suggest going through 1st one before this.

3. https://www.youtube.com/playlist?list=PLE6Wd9FR--EfW8dtjAuPoTuPcqmOV53Fu : Deep Learning Lectures from Oxford University

4. https://www.iro.umontreal.ca/~lisa/pointeurs/TR1312.pdf : This is a short book on Deep Learning written by Yoshua Bengio. It deals with theoritical aspects related to Deep Architectures. Great book though.

5. http://www.deeplearningbook.org/ : This web page has a book draft written by Yoshua Bengio and Ian Goodfellow. Later person is author of Theano library. This is holy bible on Deep Learning.

6. http://cs231n.stanford.edu/ : Deep Learning for Vision by Stanford. Useful resources for Deep Learning for Vision.

7. http://videolectures.net/yoshua_bengio/ : Video Lectures By Yoshua Bengio on Theoritical Aspects of Deep Learning. They are counterparts of resource [4].

8. http://videolectures.net/geoffrey_e_hinton/ : Video Lectures by the GodFather Geoffrey Hinton on introduction to Deep Learning and some advanced stuff too.

9. https://github.com/ChristosChristofidis/awesome-deep-learning : Good collection of resources.

10. http://deeplearning.net/reading-list/ : Reading resources

11. http://www.cs.toronto.edu/~hinton/csc2515/deeprefs.html : Reading list by Hinton

12. http://videolectures.net/mlss05us_lecun_ebmli/ : Intro to Energy based model by Yann Lecunn.

13. http://videolectures.net/kdd2014_bengio_deep_learning/?q=ICLR# : Yoshua Bengio's lecture series recorded in KDD' 14.

14. http://videolectures.net/nips09_collobert_weston_dlnl/ : Ronan Collobert lecture (it's quite old new, from 2008 but I think it is still useful).

15. https://www.youtube.com/watch?v=eixGKz0Asr8 : Lecture series by Chris Manning and Richard Socher given at NAACL 2013

16. https://www.youtube.com/watch?v=AmG4jzmBZ88 : Lecture series for DL4NLP with some practical guidelines.

17. https://blog.wtf.sg/2014/08/24/nlp-with-neural-networks/ : Blogpost on some DL applications.

18. http://lamda.nju.edu.cn/weixs/project/CNNTricks/CNNTricks.html : Some useful tricks for training Neural Networks

19. http://cs224d.stanford.edu/lectures/CS224d-Lecture11.pdf : Short notes on backprop and word embeddings

20. http://cilvr.nyu.edu/doku.php?id=courses:deeplearning2014:start : A course by Yann Lecunn on Deep Learning taught at NYU.

Word Embeddings related articles

1. https://www.tensorflow.org/versions/r0.7/tutorials/word2vec/index.html : Tensorflow tutorial on word2vec

2. http://textminingonline.com/getting-started-with-word2vec-and-glove : Intro to word2vec and glove

3. http://rare-technologies.com/deep-learning-with-word2vec-and-gensim/ : Getting starting with word2vec and gensim.

4. http://www.lab41.org/anything2vec/ : Great explaination of word2vec and it's relation to neural networks

5. http://www.offconvex.org/2015/12/12/word-embeddings-1/ : Intuition on word embedding methods

6. http://www.offconvex.org/2016/02/14/word-embeddings-2/ : Explains the mathy stuff behind word2vec and glove (Also contains some links pointing to some other good articles on word2vec)

7. http://textminingonline.com/getting-started-with-word2vec-and-glove-in-python : Getting started with glove and word2vec with python

8. http://www.foldl.me/2014/glove-python/ : Glove implementation details in python

9. http://videolectures.net/kdd2014_salakhutdinov_deep_learning/ : Tutorial by Ruslan

10. http://www.openu.ac.il/iscol2015/downloads/ISCOL2015_submission25_e_2.pdf : Comparing various word embedding models

11. http://clic.cimec.unitn.it/marco/publications/acl2014/baroni-etal-countpredict-acl2014.pdf : Comparision between word2vec and glove

12. https://levyomer.files.wordpress.com/2014/09/neural-word-embeddings-as-implicit-matrix-factorization.pdf : word2vec as matrix factorization

RNN related stuff

1. http://www.neutronest.moe/2015-11-15-LSTM-survey.html

2. http://www.kdnuggets.com/2015/06/rnn-tutorial-sequence-learning-recurrent-neural-networks.html

3. http://www.wildml.com/2015/09/recurrent-neural-networks-tutorial-part-1-introduction-to-rnns/ : Series of posts explaining RNN with some code

4. http://colah.github.io/posts/2015-08-Understanding-LSTMs/ : Great post explaining LSTMs

Optimization for Neural Networks

1. http://cs231n.github.io/neural-networks-3/#update

2. http://nptel.ac.in/courses/106108056/10 : JUMP TO SECTION : Uncontstrained optimization. Has tutorials on Non-convex optimization essential in deep Learning.

3. http://online.stanford.edu/course/convex-optimization-winter-2014 : Has more convex optimization part, contains basics of Optimization

4. http://videolectures.net/deeplearning2015_schmidt_smooth_finite/ : Deep Learning Summer School optimization lecture

Datasets

1. https://bigquery.cloud.google.com/table/fh-bigquery:reddit_comments.2015_08?pli=1 : Reddit comments dataset

2. https://code.google.com/archive/p/word2vec/ : Links to unlabelled english corpus