My hand notes while I completed the 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.
A subset of papers that I found 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 blog posts/links that I 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]