Natural Language Processing with Deep Learning in Python

5.0 /5

Natural Language Processing with Deep Learning in Python
at
UDEMY
Overview

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

Skills you will learn

Who should do this course?

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.

What are the course deliverables?

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
Use Gensim to obtain pretrained word vectors and compute similarities and analogies

More about this course

In this course we are going to look at NLP (natural language processing) with deep learning.
First up is word2vec. 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.