Advanced NLP Projects with TensorFlow 2.0

This is the code repository for Advanced NLP Projects with TensorFlow 2.0 [Video], published by Packt. It contains all the supporting project files necessary to work through the video course from start to finish.

About the Video Course

Natural Language Processing (NLP) is the field of Artificial Intelligence that deals with text analysis and understanding. Some of the fields in which NLP is widely used are sentiment classification, spam detection and topic detection. Deep Learning is one of the tools that helps us solving NLP problems. This course will get you started with real world NLP projects and you will learn how to get the best from text data. We will be building and training models in real-world projects and focus on interactions between computers and humans with Tensorflow 2.0. Together we will dive deep in a collection of text, writing a jupyter notebook step by step until we obtain actionable insights and powerful visualizations. By the end of the course, you will be able to build and implement your own NLP techniques and projects effectively with much ease confidently.

Please find all the code and support files at the following GitHub repository: https://github.com/PacktPublishing/Advanced-NLP-Projects-with-TensorFlow-2.0

What You Will Learn

Create powerful NLP based Deep Learning Models with Tensorflow 2.0
Learn to implement Word2Vec and seq2seq
Design applications that deliver scores and state of the art Visualizations
Build a text classification system (can be used for spam detection, sentiment analysis, and similar problems
Build a neural machine translation system
Understand how to extract words to classify topics

Instructions and Navigation

Assumed Knowledge

To fully benefit from the coverage included in this course, you will need:
This course will be extremely helpful for developers and data analysts who need to delve into Natural Language Processing techniques and want to do so using Google’s Deep Learning framework: TensorFlow 2.0. You should have a working experience of Python and basic NLP knowledge is a must.

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