Hands-On Natural Language Processing with Pytorch

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.

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

Audience

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.

About The Author

Jibin Mathew: Jibin Mathew is a senior data scientist and machine learning researcher who has worked in the AI domain for more than 7 years. He is a serial entrepreneur and has founded multiple AI start-ups. He has a strong software engineering background and understands the complete workflow, from research to scalable production deployment. He has built solutions in the fields of healthcare, environment, finance, industrial monitoring, and retail. He has been an adviser to various companies in their AI endeavors. He was the winner of Singularity University's Global Impact Challenge 2018 and has been part of various global platforms. He is an active contributor to the community and shares his knowledge by authoring content and through blog posts