Natural Language Processing (NLP) is a complex and rapidly evolving field, and achieving mastery requires a deep understanding of the latest techniques and technologies. If you’re an NLP expert looking to take your skills to the next level, this reading list is for you. Here, we’ve compiled a list of the best books for experts in NLP, covering the most cutting-edge research and providing in-depth explanations of advanced concepts.
“Deep Learning for Natural Language Processing” by Yoav Goldberg
This book is a must-read for experts in NLP. It provides a comprehensive overview of deep learning techniques used in NLP, including neural networks, word embeddings, and sequence-to-sequence models.
From the very basic and old school to recent developments in NLP, this book is a good read. It is completely hype free, and the author highlights where various models fall short. Even for people with good knowledge of NLP, it can be a useful reference reference, especially considering all models are written with the same terminology and unified set of notations which is quite clear.
“Natural Language Processing with TensorFlow” by Thushan Ganegedara
This book is perfect for those who want to learn about NLP using TensorFlow, one of the most popular machine learning frameworks. It covers all the key concepts in NLP and provides hands-on examples that you can use to practice what you’ve learned.
The most recent edition has updated code examples for TensorFlow 2. All of the chapters have been revised to use the Keras model building API rather than low level TF operations, and new examples have been added to keep up with the state of the art.
“Transfer Learning for Natural Language Processing” by Paul Azunre
Training deep learning NLP models from scratch is costly, time-consuming, and requires massive amounts of data. Transfer Learning for Natural Language Processing, covers the latest and greatest transfer learning techniques that apply customizable pretrained models to your own NLP architectures. This book will teach you how to use transfer learning to deliver state-of-the-art results for language comprehension, even when working with small amounts of labeled data.
This book makes it self instantly useful by providing clear and accessible explanations of the transfer learning concepts you need to know, alongside hands-on examples for high quality practice.
This book is aimed at experts who want to learn about NLP using Gensim, spaCy, and Keras. It covers all the key concepts in NLP and provides hands-on examples that you can use to practice what you’ve learned.
This book balances theory with practical hands-on examples, so you can learn about and conduct your own natural language processing projects and computational linguistics.
This book is a comprehensive reference guide to the field of computational linguistics and NLP. It covers a wide range of topics, including syntactic parsing, machine translation, and sentiment analysis, and provides a solid foundation for further study.
Whether you’re a current student or experienced professional, this handbook will serve you well as a core reference on theory and application.
These books are the ultimate reading list for experts in NLP. They’ll help you stay current with the latest research and techniques, and provide you with the knowledge you need to achieve mastery in the exciting field of NLP.
Happy Reading!
