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Full Description
Build advanced NLU systems by utilizing NLP libraries such as NLTK, SpaCy, BERT, and OpenAI; ML libraries like Keras, scikit-learn, pandas, TensorFlow, and NumPy, along with visualization libraries such as Matplotlib and Seaborn.
Purchase of the print Kindle book includes a free PDF eBook
Key Features
Master NLU concepts from basic text processing to advanced deep learning techniques
Explore practical NLU applications like chatbots, sentiment analysis, and language translation
Gain a deeper understanding of large language models like ChatGPT
Book DescriptionNatural 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.What you will learn
Explore the uses and applications of different NLP techniques
Understand practical data acquisition and system evaluation workflows
Build cutting-edge and practical NLP applications to solve problems
Master NLP development from selecting an application to deployment
Optimize NLP application maintenance after deployment
Build a strong foundation in neural networks and deep learning for NLU
Who this book is forThis book is for python developers, computational linguists, linguists, data scientists, NLP developers, conversational AI developers, and students looking to learn about natural language understanding (NLU) and applying natural language processing (NLP) technology to real problems. Anyone interested in addressing natural language problems will find this book useful. Working knowledge in Python is a must.
Contents
Table of Contents
Natural Language Understanding, Related Technologies, and Natural Language Applications
Identifying Practical Natural Language Understanding Problems
Approaches to Natural Language Understanding - Rule-Based Systems, Machine Learning, and Deep Learning
Selecting Libraries and Tools for Natural Language Understanding
Natural Language Data - Finding and Preparing Data
Exploring and Visualizing Data
Selecting Approaches and Representing Data
Rule-Based Techniques
Machine Learning Part 1 - Statistical Machine Learning
Machine Learning Part 2 - Neural Networks and Deep Learning Techniques
Machine Learning Part 3 - Transformers and Large Language Models
Applying Unsupervised Learning Approaches
How Well Does It Work? - Evaluation
What to Do If the System Isn't Working
Summary and Looking to the Future
