| Summary: | Build smart language applications using Deep Learning About This Video 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 In Detail 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 Downloading the example code for this course: You can download the example code files for all Packt video courses you have purchased from your account at http://www.PacktPub.com . If you purchased this course elsewhere, you can visit http://www.PacktPub.com/support and register to have the files e-mailed directly to you.
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