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Lecture 2 | Word Vector Representations: word2vec

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Published on Apr 3, 2017

Lecture 2 continues the discussion on the concept of representing words as numeric vectors and popular approaches to designing word vectors.

Key phrases: Natural Language Processing. Word Vectors. Singular Value Decomposition. Skip-gram. Continuous Bag of Words (CBOW). Negative Sampling. Hierarchical Softmax. Word2Vec.

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Natural Language Processing with Deep Learning

Instructors:
- Chris Manning
- Richard Socher

Natural language processing (NLP) deals with the key artificial intelligence technology of understanding complex human language communication. This lecture series provides a thorough introduction to the cutting-edge research in deep learning applied to NLP, an approach that has recently obtained very high performance across many different NLP tasks including question answering and machine translation. It emphasizes how to implement, train, debug, visualize, and design neural network models, covering the main technologies of word vectors, feed-forward models, recurrent neural networks, recursive neural networks, convolutional neural networks, and recent models involving a memory component.

For additional learning opportunities please visit:
http://stanfordonline.stanford.edu/

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