Natural Language Processing with Deep Learning in Python

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Natural Language Processing with Deep Learning in Python Download

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

Natural Language Processing with Deep Learning in Python
Natural Language Processing with Deep Learning in Python
What you’ll learn
  • Understand and implement word2vec
  • Understand the CBOW method in word2vec
  • 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
  • Use Gensim to obtain pretrained word vectors and compute similarities and analogies
Requirements
  • Install Numpy, Matplotlib, Sci-Kit Learn, and Theano or 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
Description

In this course we are going to look at NLP (natural language processing) with deep learning.

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.

First up is word2vec.

In this course, I’m going to show you exactly how word2vec works, from theory to implementation, and you’ll see that it’s merely the application of skills you already know.

Word2vec is interesting because it magically maps words to a vector space where you can find analogies, like:
  • king – man = queen – woman
  • France – Paris = England – London
  • December – Novemeber = July – June

For those beginners who find algorithms tough and just want to use a library, we will demonstrate the use of the Gensim library to obtain pre-trained word vectors, compute similarities and analogies, and apply those word vectors to build text classifiers.

 

We are also going to look at the GloVe method, which also finds word vectors, but uses a technique called matrix factorization, which is a popular algorithm for recommender systems.

Amazingly, the word vectors produced by GLoVe are just as good as the ones produced by word2vec, and it’s way easier to train.

We will also look at some classical NLP problems, like parts-of-speech tagging and named entity recognition, and use recurrent neural networks to solve them. You’ll see that just about any problem can be solved using neural networks, but you’ll also learn the dangers of having too much complexity.

Lastly, you’ll learn about recursive neural networks, which finally help us solve the problem of negation in sentiment analysis. Recursive neural networks exploit the fact that sentences have a tree structure, and we can finally get away from naively using bag-of-words.

 

“If you can’t implement it, you don’t understand it”
  • Or as the great physicist Richard Feynman said: “What I cannot create, I do not understand”.
  • My courses are the ONLY courses where you will learn how to implement machine learning algorithms from scratch
  • Other courses will teach you how to plug in your data into a library, but do you really need help with 3 lines of code?
  • After doing the same thing with 10 datasets, you realize you didn’t learn 10 things. You learned 1 thing, and just repeated the same 3 lines of code 10 times…

 

Suggested Prerequisites:
  • calculus (taking derivatives)
  • matrix addition, multiplication
  • probability (conditional and joint distributions)
  • Python coding: if/else, loops, lists, dicts, sets
  • Numpy coding: matrix and vector operations, loading a CSV file
  • neural networks and backpropagation, be able to derive and code gradient descent algorithms on your own
  • Can write a feedforward neural network in Theano or TensorFlow
  • Can write a recurrent neural network / LSTM / GRU in Theano or TensorFlow from basic primitives, especially the scan function
  • Helpful to have experience with tree algorithms

 

WHAT ORDER SHOULD I TAKE YOUR COURSES IN?:

  • Check out the lecture “Machine Learning and AI Prerequisite Roadmap” (available in the FAQ of any of my courses, including the free Numpy course)
Who this course is for:
  • Students and professionals who want to create word vector representations for various NLP tasks
  • Students and professionals who are interested in state-of-the-art neural network architectures like recursive neural networks
  • SHOULD NOT: Anyone who is not comfortable with the prerequisites.
Natural Language Processing with Deep Learning in Python Free Download

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