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NATURAL LANGUAGE PROCESSING WITH DEEP LEARNING IN PYTHON

NATURAL LANGUAGE PROCESSING WITH DEEP LEARNING IN PYTHON


Complete guide on deriving and implementing word2vec, GLoVe, word embeddings, and sentiment analysis with recursive nets

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Created by Lazy Programmer Inc.
Last updated 5/2017
English
What Will I Learn?
  • Understand and implement word2vec
  • Understand the CBOW method in word2vec
  • Understand the skip-gram method in word2vec
  • Understand the negative sampling optimization in word2vec
  • Understand and implement GLoVe using gradient descent and alternating least squares
  • Use recurrent neural networks for parts-of-speech tagging
  • Use recurrent neural networks for named entity recognition
  • Understand and implement recursive neural networks for sentiment analysis
  • Understand and implement recursive neural tensor networks for sentiment analysis
Requirements
  • Install Numpy, Matplotlib, Sci-Kit Learn, Theano, and TensorFlow (should be extremely easy by now)
  • Understand backpropagation and gradient descent, be able to do it on your own.
  • Code a recurrent neural network in Theano
  • Code a feedforward neural network in Theano

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