Best Natural Language Processing Books
A list of my personal book recommendations for NLP, for both practitioners and theorists
1. Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems
Author: by Aurélien Géron
Published at: O'Reilly Media; 2nd edition (October 15, 2019)
Through a series of recent breakthroughs, deep learning has boosted the entire field of machine learning. Now, even programmers who know close to nothing about this technology can use simple, efficient tools to implement programs capable of learning from data.
This practical book shows you how. By using concrete examples, minimal theory, and two production-ready Python frameworksScikit-Learn and TensorFlowauthor Aurlien Gron helps you gain an intuitive understanding of the concepts and tools for building intelligent systems. You’ll learn a range of techniques, starting with simple linear regression and progressing to deep neural networks.
With exercises in each chapter to help you apply what you’ve learned, all you need is programming experience to get started. Explore the machine learning landscape, particularly neural netsUse Scikit-Learn to track an example machine-learning project end-to-endExplore several training models, including support vector machines, decision trees, random forests, and ensemble methodsUse the TensorFlow library to build and train neural netsDive into neural net architectures, including convolutional nets, recurrent nets, and deep reinforcement learningLearn techniques for training and scaling deep neural nets.
2. The Hundred-Page Machine Learning Book
Author: by Andriy Burkov
Published at: Andriy Burkov (January 13, 2019)
Peter Norvig, Research Director at Google, co-author of AIMA, the most popular AI textbook in the world: “Burkov has undertaken a very useful but impossibly hard task in reducing all of machine learning to 100 pages. He succeeds well in choosing the topics both theory and practice that will be useful to practitioners, and for the reader who understands that this is the first 100 (or actually 150) pages you will read, not the last, provides a solid introduction to the field.”Aurlien Gron, Senior AI Engineer, author of the bestseller Hands-On Machine Learning with Scikit-Learn and TensorFlow: “The breadth of topics the book covers is amazing for just 100 pages (plus few bonus pages!.
Burkov doesn’t hesitate to go into the math equations: that’s one thing that short books usually drop. I really liked how the author explains the core concepts in just a few words. The book can be very useful for newcomers in the field, as well as for old-timers who can gain from such a broad view of the field.”Karolis Urbonas, Head of Data Science at Amazon: “A great introduction to machine learning from a world-class practitioner.”Chao Han, VP, Head of R&D at Lucidworks: “I wish such a book existed when I was a statistics graduate student trying to learn about machine learning.”Sujeet Varakhedi, Head of Engineering at eBay: “Andriy’s book does a fantastic job of cutting the noise and hitting the tracks and full speed from the first page.”Deepak Agarwal, VP of Artificial Intelligence at LinkedIn: “A wonderful book for engineers who want to incorporate ML in their day-to-day work without necessarily spending an enormous amount of time.”Vincent Pollet, Head of Research at Nuance: “The Hundred-Page Machine Learning Book is an excellent read to get started with Machine Learning.”Gareth James, Professor of Data Sciences and Operations, co-author of the bestseller An Introduction to Statistical Learning, with Applications in R: “This is a compact how to do data science manual and I predict it will become a go-to resource for academics and practitioners alike.
3. Python Machine Learning: Machine Learning and Deep Learning with Python, scikit-learn, and TensorFlow 2, 3rd Edition
Author: by Sebastian Raschka
Published at: Packt Publishing (December 12, 2019)
Applied machine learning with a solid foundation in theory. Revised and expanded for TensorFlow 2, GANs, and reinforcement learning. Key FeaturesThird edition of the bestselling, widely acclaimed Python machine learning bookClear and intuitive explanations take you deep into the theory and practice of Python machine learningFully updated and expanded to cover TensorFlow 2, Generative Adversarial Network models, reinforcement learning, and best practicesBook DescriptionPython Machine Learning, Third Edition is a comprehensive guide to machine learning and deep learning with Python.
It acts as both a step-by-step tutorial, and a reference you’ll keep coming back to as you build your machine learning systems. Packed with clear explanations, visualizations, and working examples, the book covers all the essential machine learning techniques in depth.
While some books teach you only to follow instructions, with this machine learning book, Raschka and Mirjalili teach the principles behind machine learning, allowing you to build models and applications for yourself. Updated for TensorFlow 2. 0, this new third edition introduces readers to its new Keras API features, as well as the latest additions to scikit-learn.
4. Introduction to Machine Learning with Python: A Guide for Data Scientists
Author: by Andreas C. Müller
Published at: O'Reilly Media; 1st edition (November 1, 2016)
Machine learning has become an integral part of many commercial applications and research projects, but this field is not exclusive to large companies with extensive research teams. If you use Python, even as a beginner, this book will teach you practical ways to build your own machine learning solutions.
With all the data available today, machine learning applications are limited only by your imagination. You’ll learn the steps necessary to create a successful machine-learning application with Python and the scikit-learn library. Authors Andreas Mller and Sarah Guido focus on the practical aspects of using machine learning algorithms, rather than the math behind them.
Familiarity with the NumPy and matplotlib libraries will help you get even more from this book. With this book, you’ll learn:Fundamental concepts and applications of machine learningAdvantages and shortcomings of widely used machine learning algorithmsHow to represent data processed by machine learning, including which data aspects to focus onAdvanced methods for model evaluation and parameter tuningThe concept of pipelines for chaining models and encapsulating your workflowMethods for working with text data, including text-specific processing techniquesSuggestions for improving your machine learning and data science skills.
5. Transformers for Natural Language Processing: Build innovative deep neural network architectures for NLP with Python, PyTorch, TensorFlow, BERT, RoBERTa, and more
Author: by Denis Rothman
Published at: Packt Publishing (January 29, 2021)
Become an AI language understanding expert by mastering the quantum leap of Transformer neural network modelsKey FeaturesBuild and implement state-of-the-art language models, such as the original Transformer, BERT, T5, and GPT-2, using concepts that outperform classical deep learning modelsGo through hands-on applications in Python using Google Colaboratory Notebooks with nothing to install on a local machineLearn training tips and alternative language understanding methods to illustrate important key conceptsBook DescriptionThe transformer architecture has proved to be revolutionary in outperforming the classical RNN and CNN models in use today.
With an apply-as-you-learn approach, Transformers for Natural Language Processing investigates in vast detail the deep learning for machine translations, speech-to-text, text-to-speech, language modeling, question answering, and many more NLP domains with transformers. The book takes you through NLP with Python and examines various eminent models and datasets within the transformer architecture created by pioneers such as Google, Facebook, Microsoft, OpenAI, and Hugging Face.
6. AI and Machine Learning for Coders: A Programmer's Guide to Artificial Intelligence
Author: by Laurence Moroney
Published at: O'Reilly Media; 1st edition (October 27, 2020)
If you’re looking to make a career move from programmer to AI specialist, this is the ideal place to start. Based on Laurence Moroney’s extremely successful AI courses, this introductory book provides a hands-on, code-first approach to help you build confidence while you learn key topics.
You’ll understand how to implement the most common scenarios in machine learning, such as computer vision, natural language processing (NLP), and sequence modeling for web, mobile, cloud, and embedded runtimes. Most books on machine learning begin with a daunting amount of advanced math.
This guide is built on practical lessons that let you work directly with the code. You’ll learn:How to build models with TensorFlow using skills that employers desireThe basics of machine learning by working with code samplesHow to implement computer vision, including feature detection in imagesHow to use NLP to tokenize and sequence words and sentencesMethods for embedding models in Android and iOSHow to serve models over the web and in the cloud with TensorFlow Serving
7. Machine Learning Engineering
Author: by Andriy Burkov
Published at: True Positive Inc. (September 5, 2020)
From the author of a world bestseller published in eleven languages, The Hundred-Page Machine Learning Book, this new book by Andriy Burkov is the most complete applied AI book out there. It is filled with best practices and design patterns of building reliable machine learning solutions that scale.
Andriy Burkov has a Ph.D. In AI and is the leader of a machine learning team at Gartner. This book is based on Andriy’s own 15 years of experience in solving problems with AI as well as on the published experience of the industry leaders.
Here’s what Cassie Kozyrkov, Chief Decision Scientist at Google tells about the book in the Foreword:”You’re looking at one of the few true Applied Machine Learning books out there. That’s right, you found one! A real applied needle in the haystack of research-oriented stuff.
Excellent job, dear reader… Unless what you were actually looking for is a book to help you learn the skills to design general-purpose algorithms, in which case I hope the author won’t be too upset with me for telling you to flee now and go pick up pretty much any other machine learning book.
8. TinyML: Machine Learning with TensorFlow Lite on Arduino and Ultra-Low-Power Microcontrollers
Author: by Pete Warden
Published at: O'Reilly Media; 1st edition (January 7, 2020)
Deep learning networks are getting smaller.Much smaller. The Google Assistant team can detect words with a model just 14 kilobytes in sizesmall enough to run on a microcontroller. With this practical book you’ll enter the field of TinyML, where deep learning and embedded systems combine to make astounding things possible with tiny devices.
Pete Warden and Daniel Situnayake explain how you can train models small enough to fit into any environment. Ideal for software and hardware developers who want to build embedded systems using machine learning, this guide walks you through creating a series of TinyML projects, step-by-step.
No machine learning or microcontroller experience is necessary. Build a speech recognizer, a camera that detects people, and a magic wand that responds to gesturesWork with Arduino and ultra-low-power microcontrollersLearn the essentials of ML and how to train your own modelsTrain models to understand audio, image, and accelerometer dataExplore TensorFlow Lite for Microcontrollers, Google’s toolkit for TinyMLDebug applications and provide safeguards for privacy and securityOptimize latency, energy usage, and model and binary size
9. Building Machine Learning Pipelines: Automating Model Life Cycles with TensorFlow
Author: by Hannes Hapke
Published at: O'Reilly Media; 1st edition (August 4, 2020)
Learn the skills necessary to design, build, and deploy applications powered by machine learning (ML). Through the course of this hands-on book, you’ll build an example ML-driven application from initial idea to deployed product. Data scientists, software engineers, and product managersincluding experienced practitioners and novices alikewill learn the tools, best practices, and challenges involved in building a real-world ML application step by step.
Author Emmanuel Ameisen, an experienced data scientist who led an AI education program, demonstrates practical ML concepts using code snippets, illustrations, screenshots, and interviews with industry leaders. Part I teaches you how to plan an ML application and measure success.
Part II explains how to build a working ML model. Part III demonstrates ways to improve the model until it fulfills your original vision. Part IV covers deployment and monitoring strategies. This book will help you:Define your product goal and set up a machine learning problemBuild your first end-to-end pipeline quickly and acquire an initial datasetTrain and evaluate your ML models and address performance bottlenecksDeploy and monitor your models in a production environment
11. You Look Like a Thing and I Love You: How Artificial Intelligence Works and Why It's Making the World a Weirder Place
Author: by Janelle Shane
Published at: Voracious; Illustrated edition (November 5, 2019)
As heard on NPR’s “Science Friday,” discover the book recommended by Malcolm Gladwell, Susan Cain, Daniel Pink, and Adam Grant: an “accessible, informative, and hilarious” introduction to the weird and wonderful world of artificial intelligence (Ryan North). “You look like a thing and I love you” is one of the best pickup lines ever …
According to an artificial intelligence trained by scientist Janelle Shane, creator of the popular blog AI Weirdness. She creates silly AIs that learn how to name paint colors, create the best recipes, and even flirt (badly) with humansall to understand the technology that governs so much of our daily lives.
We rely on AI every day for recommendations, for translations, and to put cat ears on our selfie videos. We also trust AI with matters of life and death, on the road and in our hospitals. But how smart is AI really…
And how does it solve problems, understand humans, and even drive self-driving cars? Shane delivers the answers to every AI question you’ve ever asked, and some you definitely haven’t. Like, how can a computer design the perfect sandwich? What does robot-generated Harry Potter fan-fiction look like?
12. Natural Language Processing in Action: Understanding, analyzing, and generating text with Python
Author: by Hobson Lane
Published at: Manning Publications; 1st edition (April 14, 2019)
Summary Natural Language Processing in Action is your guide to creating machines that understand human language using the power of Python with its ecosystem of packages dedicated to NLP and AI. Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications.
All examples are included in the open source `nlpia` package on python.Org and github. Com, complete with a conda environment and Dockerfile to help you get going quickly on any platform. About the Technology Recent advances in deep learning empower applications to understand text and speech with extreme accuracy.The result?
Chatbots that can imitate real people, meaningful resume-to-job matches, superb predictive search, and automatically generated document summaries-all at a low cost. New techniques, along with accessible tools like Keras and TensorFlow, make professional-quality NLP easier than ever before. About the Book Natural Language Processing in Action is your guide to building machines that can read and interpret human language.
13. Deep Reinforcement Learning Hands-On: Apply modern RL methods to practical problems of chatbots, robotics, discrete optimization, web automation, and more, 2nd Edition
Author: by Maxim Lapan
Published at: Packt Publishing (January 31, 2020)
New edition of the bestselling guide to deep reinforcement learning and how it’s used to solve complex real-world problems. Revised and expanded to include multi-agent methods, discrete optimization, RL in robotics, advanced exploration techniques, and more Key Features Second edition of the bestselling introduction to deep reinforcement learning, expanded with six new chapters Learn advanced exploration techniques including noisy networks, pseudo-count, and network distillation methods Apply RL methods to cheap hardware robotics platforms Book Description Deep Reinforcement Learning Hands-On, Second Edition is an updated and expanded version of the bestselling guide to the very latest reinforcement learning (RL) tools and techniques.
It provides you with an introduction to the fundamentals of RL, along with the hands-on ability to code intelligent learning agents to perform a range of practical tasks. With six new chapters devoted to a variety of up-to-the-minute developments in RL, including discrete optimization (solving the Rubik’s Cube), multi-agent methods, Microsoft’s TextWorld environment, advanced exploration techniques, and more, you will come away from this book with a deep understanding of the latest innovations in this emerging field.
14. Practical Natural Language Processing: A Comprehensive Guide to Building Real-World NLP Systems
Author: by Sowmya Vajjala
Published at: O'Reilly Media; 1st edition (July 7, 2020)
Many books and courses tackle natural language processing (NLP) problems with toy use cases and well-defined datasets. But if you want to build, iterate, and scale NLP systems in a business setting and tailor them for particular industry verticals, this is your guide.
Software engineers and data scientists will learn how to navigate the maze of options available at each step of the journey. Through the course of the book, authors Sowmya Vajjala, Bodhisattwa Majumder, Anuj Gupta, and Harshit Surana will guide you through the process of building real-world NLP solutions embedded in larger product setups.
You’ll learn how to adapt your solutions for different industry verticals such as healthcare, social media, and retail. With this book, you’ll:Understand the wide spectrum of problem statements, tasks, and solution approaches within NLPImplement and evaluate different NLP applications using machine learning and deep learning methodsFine-tune your NLP solution based on your business problem and industry verticalEvaluate various algorithms and approaches for NLP product tasks, datasets, and stagesProduce software solutions following best practices around release, deployment, and DevOps for NLP systemsUnderstand best practices, opportunities, and the roadmap for NLP from a business and product leader’s perspective
15. Accelerated Spanish: Learn fluent Spanish with a proven accelerated learning system (1)
Author: by Timothy Moser
Published at: Kamel Press, LLC (October 1, 2016)
If you’re like the majority of US high school graduates, you have (1) studied Spanish at some point and (2) already forgotten most of it. It’s time for a new language-learning method: One that teaches Spanish the way native speakers talk, using advanced mnemonic techniques you won’t be able to forget.
Accelerated Spanish is the proven method that has trained hundreds of students, bringing dozens to fluency in a very short period of time. This three-volume system has the potential to make you fully fluent in Spanish. Volume One teaches you to think like a native Spanish speaker and gives you the vocabulary that makes up 50% of the Spanish language.
Volume Two builds on that foundation, giving you 80% of the vocabulary, and it allows you to converse comfortably on a variety of subjects. Volume Three enables you to reach practical fluency in Spanish, enough to have an intelligent conversation with a native speaker on nearly any subject.
As you find yourself engulfed in an imaginary world with a yellow sky, demented shopkeepers, and clumsy stuffed pandas, your mind and senses will be challenged at every moment. From the very first chapter, you will begin to understand why truly getting into the mindset of another language requires your mind to experience a new world.