Natural Language Understanding with Python
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Natural Language Understanding with Python
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Overview of this book
Natural Language Understanding facilitates the organization and structuring of language allowing computer systems to effectively process textual information for various practical applications. Natural Language Understanding with Python will help you explore practical techniques for harnessing NLU to create diverse applications.
with step-by-step explanations of essential concepts and practical examples, you’ll begin by learning about NLU and its applications. You’ll then explore a wide range of current NLU techniques and their most appropriate use-case. In the process, you’ll be introduced to the most useful Python NLU libraries. Not only will you learn the basics of NLU, you’ll also discover practical issues such as acquiring data, evaluating systems, and deploying NLU applications along with their solutions. The book is a comprehensive guide that’ll help you explore techniques and resources that can be used for different applications in the future.
By the end of this book, you’ll be well-versed with the concepts of natural language understanding, deep learning, and large language models (LLMs) for building various AI-based applications.
Table of Contents (21 chapters)
Preface
Part 1: Getting Started with Natural Language Understanding Technology
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Chapter 1: Natural Language Understanding, Related Technologies, and Natural Language Applications
Chapter 2: Identifying Practical Natural Language Understanding Problems
Part 2:Developing and Testing Natural Language Understanding Systems
Chapter 3: Approaches to Natural Language Understanding – Rule-Based Systems, Machine Learning, and Deep Learning
Chapter 4: Selecting Libraries and Tools for Natural Language Understanding
Chapter 5: Natural Language Data – Finding and Preparing Data
Chapter 6: Exploring and Visualizing Data
Chapter 7: Selecting Approaches and Representing Data
Chapter 8: Rule-Based Techniques
Chapter 9: Machine Learning Part 1 – Statistical Machine Learning
Chapter 10: Machine Learning Part 2 – Neural Networks and Deep Learning Techniques
Chapter 11: Machine Learning Part 3 – Transformers and Large Language Models
Chapter 12: Applying Unsupervised Learning Approaches
Chapter 13: How Well Does It Work? – Evaluation
Part 3: Systems in Action – Applying Natural Language Understanding at Scale
Chapter 14: What to Do If the System Isn’t Working
Chapter 15: Summary and Looking to the Future
Index
Customer Reviews