Natural Language Processing with Deep Learning in Python

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Natural Language Processing with Deep Learning in Python, Complete guide on deriving and implementing word2vec, GloVe, word embeddings, and sentiment analysis with recursive nets

Created by Lazy Programmer Inc.

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What you'll 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 derive and code the equations on your own
• Code a recurrent neural network from basic primitives in Theano (or Tensorflow), especially the scan function
• Code a feedforward neural network in Theano (or Tensorflow)
• Helpful to have experience with tree algorithms