Book Image

Hands-On Natural Language Processing with Pytorch [Video]

By : Jibin Mathew
Book Image

Hands-On Natural Language Processing with Pytorch [Video]

By: Jibin Mathew

Overview of this book

The main goal of this course is to train you to perform complex NLP tasks (and build intelligent language applications) using Deep Learning with PyTorch. You will build two complete real-world NLP applications throughout the course. The first application is a Sentiment Analyzer that analyzes data to determine whether a review is positive or negative towards a particular movie. You will then create an advanced Neural Translation Machine that is a speech translation engine, using Sequence to Sequence models with the speed and flexibility of PyTorch to translate given text into different languages. By the end of the course, you will have the skills to build your own real-world NLP models using PyTorch's Deep Learning capabilities. The code bundle for this video course is available at - https://github.com/PacktPublishing/Hands-On-Natural-Language-Processing-with-Pytorch This course uses Python 3.6, Pytorch 1.0, NLTK 3.3.0, and Spacy 2.0 , while not the latest version available, it provides relevant and informative content for legacy users of PyTorch.
Table of Contents (6 chapters)
Chapter 6
Improve the Neural Machine Translation with Attention Networks
Content Locked
Section 2
Implementing seq2seq – Encoder
Discuss the changes in architecture and understanding the interaction of attention layer with the encoder and decoder. - Attention Layer and the Attention Vector - Defining Attention class - Tensor transformations for attention