Hands-On Natural Language Processing with Pytorch

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.

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.

About the Author:

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.   

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

Who Should Attend!

• 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!

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