Hand Notes

Credit goes to © Omar Sharif for everything in this page. These are handmade notes of him on the popular Deep Learning Specialization on Coursera.

• Neural Networks and Deep Learning Note.
• Improving Deep Neural Networks Note.
• Structuring Machine Learning Projects Note.
• Convolutional Neural Networks Note.
• Sequence Models Note.

NLP Resources

List of few books, courses and blog posts that is really helpful.

• Collected Advices
  • Collection of Advices [link]
  • Advice for Researchers and Students [link]
  • Career advice by [Andrew Ng]
  • Advice for Research Students [Jason Eisner]
• Important Books
  • Speech and Language Processing [link]
  • Machine Learning with TensorFlow [link]
  • Deep Learning with Python [link]
  • Natural Language Processing in Action [link]
  • Deep Learning [link]
• Key Courses
  • CMU Advanced NLP course [Advanced NLP]
  • Deep Learning Specialization (all courses) [link]
  • NTU NLP course [Deep NLP]
  • TensorFlow Specialization [DeepLearning.AI]
  • Natural Language Processing with Deep Learning [CS224n]
• Guidelines to Follow
  • ACL Year-Round Mentorship [Link]
  • Awesome illustrations of [Jay Alammar].
  • Colah’s Blog [Link].
  • Machine Learning Mystery [Jason Brownlee]

Must Read NLP Papers

A subset of papers that are useful in clarifying my understanding of various NLP topics.

* A Neural Probabilistic Language Model 
* [Word2Vec]/[Negative Sampling]/[GloVe] 
* Learning Phrase Representations using RNN Encoder–Decoder for Statistical Machine Translation 
* Sequence to Sequence Learning with Neural Networks 
* Neural Machine Translation by Jointly Learning to Align and Translate (Paper that introduces Attention) 
* Effective Approaches to Attention-based Neural Machine Translation 
* Attention Is All You Need
* [ELMo]/[BERT]/[RoBERTa] 
* [ELECTRA]/[ALBERT]/[XLNet]
* [GPT]/[GPT-2]/[GPT-3]
* T5 (an awesome paper)

Few Useful Links

Few blog posts/links that are found really useful to understand various fundamental concepts of NLP.

* Andrej Karpathy's coding-based backpropagation post [Link]
* Andrej Karpathy's blog on RNNs [Link]
* Understanding LSTM Networks [Link]
* The Illustrated Word2vec [Link]
* Mechanics of Seq2seq Models With Attention [Link]
* The Illustrated Transformer [Link]
* The Annotated Transformer [Link]
* Visualizing Transformer Language Models [Link]
* The State of Transfer Learning in NLP [Link]
* The Illustrated BERT, ELMo, and co. [Link]
* A Visual Guide to Using BERT [Link]
* Various BERT Pre-Training Methods [Link]