NATURAL LANGUAGE PROCESSING WITH DEEP LEARNING IN PYTHON

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NATURAL LANGUAGE PROCESSING WITH DEEP LEARNING IN PYTHON

Natural Language: In this course you will learn the complete guide on deriving & implementing word2vec, Glove and Word embeddings or sentiment analysis with recursive nets.

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Created By: Lazy Programmer Inc

Last Updated: 5/2017

English

What will you learn in this course ?

  • You will Learn and Understand the implement word2vec.
  • You will learn and Understand the CBOW method in word2vec.
  • You will learn and Understand the skip-gram method in word2vec.
  • You will learn and Understand the negative sampling optimization in word2vec
  • You will learn and Understand the  implement GLoVe using gradient descent & alternating least squares.
  • You will learn and Use recurrent neural networks for parts-of-speech tagging.
  • Learn how to Use recurrent neural networks for named entity recognition.
  • You will learn and Understand the  implement recursive neural networks for sentiment analysis.
  • You will learn and Understand the implement recursive neural tensor networks for sentiment analysis.
Requirements=>
  • You will need to install Numpy and Matplotlib or Sci-kit learn, Theano & Tensorflow.
  • You will need the code a recurrent neural network in the Theano.
  • You will need the code a feedforward neural network in theano.
Who is the target audience?
  • Anybody who wants to create word vector representations for various NLP TASKS.
  • Anybody who is interested in state of the art neural network architectures such as neural networks.
  • Anybody who is not comfortable with the prerequisites.

Size: 665.40M

https://www.udemy.com/natural-language-processing-with-deep-learning-in-python/.

 

 

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