Hands-On Natural Language Processing with PyTorch: Build smart language applications using Deep Learning

Jibin Mathew

€8.99 ~~€120.99~~

Video Jan 2019 2hrs 24mins 1st Edition

Jibin Mathew

€8.99 ~~€120.99~~

Video Jan 2019 2hrs 24mins 1st Edition

What do you get with a video?

Download this video in MP4 format

Access this title in our online reader with advanced features

DRM FREE - Read whenever, wherever and however you want

View table of contents

Download Code

Key benefits

Hands-on projects covering various NLP applications
Practical guidance on data preprocessing and model implementation
In-depth understanding of advanced NLP concepts
Expert insights into PyTorch and its applications in NLP

Description

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.

Who is this book for?

If you’re a developer, researcher or aspiring AI data scientist ready to dive deeper into this rapidly growing area of artificial intelligence then this course is for you! Some basic Machine learning background & experience in programming with Python is required.