On October 30, 2022, the launch of ChatGPT has attracted widespread attention around the world. Microsoft founder Bill Gates (Bill Gates) believes that "ChatGTP is as important as the Internet". As an artificial intelligence system, ChatGPT can accurately identify user intent, engage in dialogue with users and provide valuable information and assistance. ChatGPT has great application potential in many industries, such as education, creation, customer service, technical support, etc. Since the 1950s, humans have been researching and developing artificial intelligence, hoping that computers can simulate human intelligence, provide automated solutions to solve complex tasks, and finally achieve artificial general intelligence (AGI), which can learn and process Artificial intelligence for any task. The emergence of ChatGPT has made the realization of AGI dawn and has become a very promising path. The cornerstone of this path is natural language processing (NLP) based on deep learning.
In recent years, with the breakthrough of deep learning in natural language processing, it has replaced traditional machine learning and become the mainstream method of natural language processing. Various deep learning models and technologies for natural language processing have been continuously proposed, especially the emergence of large language model (large language model, LLM) and ChatGPT, and the research on natural language processing has exploded. However, it is difficult to fully understand deep learning-based natural language processing, mainly for the following reasons:
Natural language processing based on deep learning needs to cover multiple domain knowledge, including natural language processing, machine learning and deep learning, etc., and its wide coverage of knowledge domains makes learning difficult.
Natural language processing based on deep learning is showing explosive development. The continuous emergence of various new models and technologies makes people feel at a loss about the core direction and technology of learning.
Natural language processing based on deep learning involves many complex concepts and models, which are difficult to explain clearly.
Although there are already many books on natural language processing on the market, there is still a lack of an introductory book that comprehensively introduces natural language processing based on deep learning and is easy to understand for the above-mentioned learning difficulties. Therefore, we published Deep Learning in Natural Language Processing: From Word Representations to ChatGPT.
This book focuses on the core knowledge and applications of natural language processing based on deep learning, rather than a comprehensive introduction to all knowledge points of natural language processing. It aims to help readers fully understand and build the core knowledge tree of deep learning-based natural language processing for a quick start. Once readers have grasped the core content of the knowledge tree (trunk and main branches), they can self-assess whether it is worth their time to learn before learning new knowledge details (leaves). This book will provide a targeted guide to help beginners build a solid foundation before diving into the field of natural language processing research.
The author of this book has rich theoretical research experience (judges of natural language processing journals and conferences (eg, ACL, EMNLP, KDD)) and front-line practical experience (senior engineer of Dachang) in the field of deep learning and natural language processing. He integrates these valuable experiences into this book and presents it to readers in an intuitive and easy-to-understand manner. Help readers comprehensively and deeply understand natural language processing based on deep learning. After reading this book, a beginner can have the relevant knowledge reserves required by a natural language processing engineer in a large factory or a graduate student in natural language processing in a university.
The book has a clear structure, consisting of multiple chapters, each dedicated to introducing and discussing a key topic. The book not only provides a comprehensive introduction to deep learning and natural language processing, but also deeply discusses the key technologies and application areas of deep learning in natural language processing. Whether you are a student in a tertiary institution, a scholar or engineer engaged in natural language processing research, or a reader interested in artificial intelligence systems such as ChatGPT, this book will become one of your indispensable reference books. By reading this book, you will establish a comprehensive understanding of the theoretical basis and practical skills of deep learning in natural language processing, so as to better apply and promote the development of the field of natural language processing.
What this book is about
First of all, this book provides a comprehensive and systematic introduction to the basics of natural language processing based on deep learning. Readers will learn the fundamentals of machine learning, deep learning, and natural language processing, including commonly used machine learning algorithms, the foundation of deep learning, and the advantages and challenges of deep learning in natural language processing. This part aims to help readers establish a basic understanding of deep learning and natural language processing, and prepare for the study of subsequent chapters.
Subsequently, the book deeply explores the core framework and technology of deep learning in natural language processing, including word representation (for example, Word2Vec, ELMo, GPT, BERT and T5, etc.), attention mechanism, transfer learning and reinforcement learning, etc. With detailed explanations and examples, it provides a foundation for a variety of natural language processing tasks.
Next, the book focuses on practical applications of deep learning-based natural language processing. The author details the application cases of deep learning in major natural language processing tasks such as machine translation, text summarization, automatic question answering, dialogue systems, and sentiment analysis. Each application field is equipped with detailed practical cases and analysis to help readers gain a deep understanding of the application methods, optimization strategies, and actual effects of deep learning in different tasks.
Finally, the book introduces ChatGPT. The previous chapters of this book are very helpful for understanding ChatGPT. The author deeply introduces the core technologies behind ChatGPT, such as large-scale language models and reinforcement learning methods based on artificial feedback, as well as the ChatGPT training and modeling process, and looks forward to the development of ChatGPT.
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Who should read this book
About the Author
Lei Zhang
is currently working in Meta Company on the research and development of machine learning algorithms. Ph.D. in computer science from the University of Illinois at Chicago, USA. His main research areas are natural language processing, machine learning and data mining. He has published more than 20 academic articles in academic journals and conferences at home and abroad, obtained a number of U.S. patents, co-authored 4 books on text data mining and big data computing, including Mining Text Data, and has been invited as a natural language processing expert for a long time. Reviewer of international journals and member of program committee of international conferences.
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