Top NLP Books: In this post, we’ll look at some exciting books focusing on new research and application areas in Natural Language Processing.
The demand for language models in natural language processing (NLP) is increasing and has pushed forward the limits of language understanding and generation. Thanks to the latest research advances in transfer learning and applying transformers architecture to different NLP tasks, language models have become immensely popular in 2021. Access to data and the increase in computational power are allowing practitioners to achieve meaningful results in different domains such as healthcare, finance, and media, among others. The growth has led to new and improved algorithms, helping researchers and developers to make an impact in emerging NLP topic areas. Here are some exciting NLP books to read in 2021:
BERT has revolutionized the world of natural language processing (NLP) with promising results. This book is an introductory guide that will help you get to grips with Google BERT’s architecture along with practical examples.
- Explore the encoder and decoder of the transformer model
- Become well-versed with BERT along with ALBERT, RoBERTa, and DistilBERT
- Discover how to pre-train and fine-tune BERT models for several NLP tasks
Generative Pre-trained Transformer 3 (GPT-3) is a highly advanced language model from OpenAI that can generate written text, which is virtually indistinguishable from text written by humans. Whether you have a technical or non-technical background, this book will help you understand and start working with GPT-3 and the OpenAI API.
- Understand the power of potential GPT-3 language models and the risks involved
- Explore core GPT-3 use cases such as text generation, classification, and semantic search using engaging examples
- Plan and prepare a GPT-3 application for the OpenAI review process
Leverage your natural language processing skills to make sense of text. With this book, you’ll learn fundamental and advanced NLP techniques in Python that will help you to make your data fit for application in a wide variety of industries. You’ll also find recipes for overcoming common challenges in implementing NLP pipelines.
- Analyze varying complexities of text using popular Python packages such as NLTK, spaCy, sklearn, and gensim
- Implement common and not-so-common linguistic processing tasks using Python libraries
- Overcome the common challenges faced while implementing NLP pipelines
Using spaCy, an advanced industrial-grade NLP library, and machine learning-based NLP models, you can speed up business processes, make more accurate predictions, and uncover new insights from your existing data. With this book, you’ll learn how to use it and create high-impact machine learning
- Gain an overview of what spaCy offers for natural language processing
- Explore spaCy’s features and how to use them effectively
- Work through practical recipes using spaCy
Explore the accuracy and fast fine-tuning capabilities of transformer-based language models and understand how they outperform traditional machine learning-based approaches when solving challenging NLU problems. Developers working with the transformers architecture will be able to put their knowledge to work with this practical guide.
- Explore quick prototyping with up-to-date Python libraries to create effective solutions to industrial problems
- Solve advanced NLP problems such as named-entity recognition, information extraction, language generation, and conversational AI
- Monitor your model’s performance with the help of BertViz, exBERT, and TensorBoard
The book includes Python code examples for AWS AI services such as Amazon Textract and Amazon Comprehend to derive insights from unstructured text with little prior machine learning knowledge. Packed with a variety of use cases, this book will help you automate NLP workflows on AWS in a serverless manner. Whether you’re new to machine learning or an experienced practitioner, by the end of this book, you’ll have the confidence to use AWS AI services to build powerful NLP applications.
- Discover AWS AI services for NLP and how to use them to gain real-time insights
- Run Python code to use Amazon Textract and Amazon Comprehend to accelerate business outcomes
- Understand how you can integrate human-in-the-loop for custom NLP use cases
NLP has changed the way we interact with computers and it will continue to do so in the future. Although there are a lot of challenges for NLP, the discipline is developing at a very fast pace. According to industry experts, implementation still remains one of the biggest challenges of 2021. With the available information constantly growing in size and getting increasingly sophisticated, gaining exposure to key NLP research areas will help you stay aligned to continuous advancements in the domain.
About the Author:
Sunith Shetty is a data science fanatic. He helps IT developers become thought leaders in developer communities by contributing to creating expert-approved tech books. He is currently working with machine learning and AI developer communities to design and create book value propositions on popular and emerging technologies.