Language modeling with n-gram counts, the Markov assumption, and the memory costs of traditional models.
Save to open this file
Stanford CS224N: Natural Language Processing with Deep Learning
All
- 01-Introduction to NLP and Deep Learning
01-Introduction to NLP and Deep Learning
- 02-Word Vector Representations: word2vec
- 03-Advanced Word Vector Representations
- 04-Word Window Classification and Neural Networks
- 05-Backpropagation and Project Advice
- 06-Dependency Parsing
- 07-Introduction to TensorFlow
- 08-Recurrent Neural Networks and Language Models
- 09-Machine Translation and Advanced Recurrent LSTMs and GRUs
- 10-Neural Machine Translation and Models with Attention
- 11-Gated Recurrent Units and Further Topics in NMT
- 12-End-to-End Models for Speech Processing
- 13-Convolutional Neural Networks
- 14-Tree Recursive Neural Networks and Constituency Parsing
- 15-Coreference Resolution
- 16-Dynamic Neural Networks for Question Answering
- 17-Issues in NLP and Possible Architectures for NLP
- 18-Tackling the Limits of Deep Learning for NLP