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Hands-On Python Natural Language Processing

You're reading from Hands-On Python Natural Language Processing Explore tools and techniques to analyze and process text with a view to building real-world NLP applications

Product type Paperback

Published in Jun 2020

Publisher Packt

ISBN-13 9781838989590

Length 316 pages

Edition 1st Edition

Languages

Processing

Tools

NumPy

Concepts

Mobile Application Development

Authors (2):

Mayank Rasu

Aman Kedia

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Table of Contents (16) Chapters

Preface

1. Section 1: Introduction

2. Understanding the Basics of NLP FREE CHAPTER

3. NLP Using Python

4. Section 2: Natural Language Representation and Mathematics

5. Building Your NLP Vocabulary

6. Transforming Text into Data Structures

7. Word Embeddings and Distance Measurements for Text

8. Exploring Sentence-, Document-, and Character-Level Embeddings

9. Section 3: NLP and Learning

10. Identifying Patterns in Text Using Machine Learning

11. From Human Neurons to Artificial Neurons for Understanding Text

12. Applying Convolutions to Text

13. Capturing Temporal Relationships in Text

14. State of the Art in NLP

15. Other Books You May Enjoy

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Technical requirements

The code files for this chapter can be downloaded from the following GitHub repository: https://github.com/PacktPublishing/Hands-On-Python-Natural-Language-Processing/tree/master/Chapter03.

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Authors (2)

Kedia

Kedia

Aman Kedia is a data enthusiast and lifelong learner. He is an avid believer in Artificial Intelligence (AI) and the algorithms supporting it. He has worked on state-of-the-art problems in Natural Language Processing (NLP), encompassing resume matching and digital assistants, among others. He has worked at Oracle and SAP, trying to solve problems leveraging advancements in AI. He has four published research papers in the domain of AI.

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Rasu

Rasu

Mayank Rasu is the author of the book Hands-On Natural Language Processing with Python. He has more than 12 years of global experience as a data scientist and quantitative analyst in the investment banking domain. He has worked at the intersection of finance and technology and has developed and deployed AI-based applications in the finance domain, which include sentiment analyzer, robotics process automation, and deep learning-based document reviewers. Mayank is also an educator and has trained/mentored working professionals on applied AI.

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