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

Backpropagation through multilayer neural networks, with gradient calculations and activations, plus project and debugging guidance.Backpropagation through multilayer neural networks, including gradient calculations and activations, with project and debugging guidance.

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