Natural Language Processing with Deep Learning in Python

The promise of deep learning methods for natural language processing problems as defined by experts in the field. How to prepare text data for modeling by hand and using best-of-breed Python libraries such as the natural language toolkit or NLTK. and 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 10/2018

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Description

In this course we are going to look at advanced NLP.

Previously, you learned about some of the basics, like how many NLP problems are just regular machine learning and data science problems in disguise, and simple, practical methods like bag-of-words and term-document matrices.

These allowed us to do some pretty cool things, like detect spam emails, write poetry, spin articles, and group together similar words.

In this course I’m going to show you how to do even more awesome things. We’ll learn not just 1, but 4 new architectures in this course.

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

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Natural Language Processing with Deep Learning in Python