Navigating the NLP Landscape: A Comprehensive Introduction
This book is aimed at helping professionals apply natural language processing (NLP) techniques to their work, whether they are working on NLP projects or using NLP in other areas, such as data science. The purpose of the book is to introduce you to the field of NLP and its underlying techniques, including machine learning (ML) and deep learning (DL). Throughout the book, we highlight the importance of mathematical foundations, such as linear algebra, statistics and probability, and optimization theory, which are necessary to understand the algorithms used in NLP. The content is accompanied by code examples in Python to allow you to pre-practice, experiment, and generate some of the development presented in the book.
The book discusses the challenges faced in NLP, such as understanding the context and meaning of words, the relationships between them, and the need for labeled data. The book also mentions the recent advancements in NLP, including pre-trained language models, such as BERT and GPT, and the availability of large amounts of text data, which has led to improved performance on NLP tasks.
The book will engage you by discussing the impact of language models on the field of NLP, including improved accuracy and effectiveness in NLP tasks, the development of more advanced NLP systems, and accessibility to a broader range of people.
We will be covering the following headings in the chapter:
- What is natural language processing?
- Initial strategies in the machine processing of natural language
- A winning synergy – the coming together of NLP and ML
- Introduction to math and statistics in NLP
