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
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.
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.
Table of contents
- Chapter 1 : Up and Running with PyTorch
- Chapter 2 : Data Cleaning and Preprocessing for Sentiment Analysis
- Chapter 3 : Implement Word Embeddings with gensim
- Chapter 4 : Train RNNs and LSTMs Units for Sentiment Analysis
- Chapter 5 : Build a Neural Machine Translator
- Chapter 6 : Improve the Neural Machine Translation with Attention Networks
- Title: Hands-On Natural Language Processing with Pytorch
- Release date: January 2019
- Publisher(s): Packt Publishing
- ISBN: 9781789133974
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