Hands-On Natural Language Processing with Pytorch [Video]

More Information
Learn
  • Processing insightful information from raw data using NLP techniques with PyTorch
  • Working with PyTorch to take advantage of its maximum speed and flexibility
  • Traditional and modern NLP methods & tools like NLTK, Spacy, Word2Vec & Gensim
  • Implementing word embedding model and using it with the Gensim toolkit
  • Sequence-to-sequence models (used in translation) that read one sequence & produces another
  • Usage of LSTMs using PyTorch for Sentiment Analysis and how its different from RNNs 
  • Comparing and analysing results using Attention networks to improve your project’s performance
About

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

Style and Approach

A practical step by step approach for building intelligent language applications using NLP. You will first understand the intuition & logic behind each task then follow it with its implementation for an effective training of text & data processing with PyTorch.

Features
  • Extensive practical training to understand the combined working of NLP, deep learning, and PyTorch
  • Work with both traditional & modern NLP tools like NLTK, SpaCy & Word2Vec for creating real world NLP models.
  • Each chapter includes several code examples and illustrations for an in-depth understanding of performing complex NLP tasks
Course Length 2 hours 24 minutes
ISBN 9781789133974
Date Of Publication 31 Jan 2019

Authors

Jibin Mathew

Jibin Mathew is a Tech-Entrepreneur, Artificial Intelligence enthusiast and an active researcher. He has spent several years as a Software Solutions Architect, with a focus on Artificial Intelligence for the past 5 years. He has architected and built various solutions in Artificial Intelligence which includes solutions in Computer Vision, Natural Language Processing/Understanding and Data sciences, pushing the limits of computational performance and model accuracies. He is well versed with concepts in Machine learning and Deep learning and serves a consultant for clients from Retail, Environment, Finance and Health care.