Use modern NLP tools & techniques with Deep Learning & PyTorch to build intelligent language applications

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.

What You Will 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



https://www.packtpub.com/application-development/hands-natural-language-processing-pytorch-video