Word meaning representations, distributional similarity, and the word2vec skip-gram and CBOW models, including their objectives, gradients, and optimization.
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Stanford CS224N: Natural Language Processing with Deep Learning
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- 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