CS224N: NLP with Deep Learning (Stanford)

Stanford CS224N: Natural Language Processing with Deep Learning

Guide

All

01-Introduction to NLP and Deep Learning
01-Introduction to NLP and Deep Learning
02-Word Vector Representations: word2vec
02-Word Vector Representations: word2vec
03-Advanced Word Vector Representations
03-Advanced Word Vector Representations
04-Word Window Classification and Neural Networks
04-Word Window Classification and Neural Networks
05-Backpropagation and Project Advice
05-Backpropagation and Project Advice
06-Dependency Parsing
06-Dependency Parsing
07-Introduction to TensorFlow
07-Introduction to TensorFlow
08-Recurrent Neural Networks and Language Models
08-Recurrent Neural Networks and Language Models
09-Machine Translation and Advanced Recurrent LSTMs and GRUs
09-Machine Translation and Advanced Recurrent LSTMs and GRUs
10-Neural Machine Translation and Models with Attention
10-Neural Machine Translation and Models with Attention
11-Gated Recurrent Units and Further Topics in NMT
11-Gated Recurrent Units and Further Topics in NMT
12-End-to-End Models for Speech Processing
12-End-to-End Models for Speech Processing
13-Convolutional Neural Networks
13-Convolutional Neural Networks
14-Tree Recursive Neural Networks and Constituency Parsing
14-Tree Recursive Neural Networks and Constituency Parsing
15-Coreference Resolution
15-Coreference Resolution
16-Dynamic Neural Networks for Question Answering
16-Dynamic Neural Networks for Question Answering
17-Issues in NLP and Possible Architectures for NLP
17-Issues in NLP and Possible Architectures for NLP
18-Tackling the Limits of Deep Learning for NLP
18-Tackling the Limits of Deep Learning for NLP

Winda

Covers NLP’s remaining challenges, inference and knowledge representations, tree-structured neural models such as SPINN for natural language inference, and neural approaches to question answering, copying, and character-level processing.

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