Best Machine Theory Books
Machine learning is an intimidating subject. Knowing where to develop mastery around such a massive subject that encompasses so many fields, research topics, and applications can be the hardest part of the journey.
1. Gödel, Escher, Bach: An Eternal Golden Braid
Author: by Douglas R Hofstadter
Published at: Basic Books; Anniversary edition (February 5, 1999)
Winner of the Pulitzer PrizeA metaphorical fugue on minds and machines in the spirit of Lewis CarrollDouglas Hofstadter’s book is concerned directly with the nature of “maps” or links between formal systems. However, according to Hofstadter, the formal system that underlies all mental activity transcends the system that supports it.
If life can grow out of the formal chemical substrate of the cell, if consciousness can emerge out of a formal system of firing neurons, then so too will computers attain human intelligence. Gdel, Escher, Bach is a wonderful exploration of fascinating ideas at the heart of cognitive science: meaning, reduction, recursion, and much more.
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. Machine Learning Design Patterns: Solutions to Common Challenges in Data Preparation, Model Building, and MLOps
Author: by Valliappa Lakshmanan
Published at: O'Reilly Media; 1st edition (November 10, 2020)
SummaryGrokking Algorithms is a fully illustrated, friendly guide that teaches you how to apply common algorithms to the practical problems you face every day as a programmer. You’ll start with sorting and searching and, as you build up your skills in thinking algorithmically, you’ll tackle more complex concerns such as data compression and artificial intelligence.
Each carefully presented example includes helpful diagrams and fully annotated code samples in Python. Learning about algorithms doesn’t have to be boring! Get a sneak peek at the fun, illustrated, and friendly examples you’ll find in Grokking Algorithms on Manning Publications’ YouTube channel.
Continue your journey into the world of algorithms with Algorithms in Motion, a practical, hands-on video course available exclusively at Manning.Com (www.Manning. Com/livevideo/algorithms-in-motion). Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications.
About the TechnologyAn algorithm is nothing more than a step-by-step procedure for solving a problem. The algorithms you’ll use most often as a programmer have already been discovered, tested, and proven. If you want to understand them but refuse to slog through dense multipage proofs, this is the book for you.
5. Deep Learning for Coders with Fastai and PyTorch: AI Applications Without a PhD
Author: by Jeremy Howard
Published at: O'Reilly Media; 1st edition (August 11, 2020)
Deep learning is often viewed as the exclusive domain of math PhDs and big tech companies. But as this hands-on guide demonstrates, programmers comfortable with Python can achieve impressive results in deep learning with little math background, small amounts of data, and minimal code.How?
With fastai, the first library to provide a consistent interface to the most frequently used deep learning applications. Authors Jeremy Howard and Sylvain Gugger, the creators of fastai, show you how to train a model on a wide range of tasks using fastai and PyTorch.
You’ll also dive progressively further into deep learning theory to gain a complete understanding of the algorithms behind the scenes. Train models in computer vision, natural language processing, tabular data, and collaborative filteringLearn the latest deep learning techniques that matter most in practiceImprove accuracy, speed, and reliability by understanding how deep learning models workDiscover how to turn your models into web applicationsImplement deep learning algorithms from scratchConsider the ethical implications of your workGain insight from the foreword by PyTorch cofounder, Soumith Chintala.
6. Advances in Financial Machine Learning
Author: by Marcos Lopez de Prado
Published at: Wiley; 1st edition (February 21, 2018)
Machine learning (ML) is changing virtually every aspect of our lives. Today ML algorithms accomplish tasks that until recently only expert humans could perform. As it relates to finance, this is the most exciting time to adopt a disruptive technology that will transform how everyone invests for generations.
Readers will learn how to structure Big data in a way that is amenable to ML algorithms; how to conduct research with ML algorithms on that data; how to use supercomputing methods; how to backtest your discoveries while avoiding false positives.
The book addresses real life problems faced by practitioners on a daily basis, and explains scientifically sound solutions using math, supported by code and examples. Readers become active users who can test the proposed solutions in their particular setting. Written by a recognized expert and portfolio manager, this book will equip investment professionals with the groundbreaking tools needed to succeed in modern finance.
7. Practical Threat Intelligence and Data-Driven Threat Hunting: A hands-on guide to threat hunting with the ATT&CK™ Framework and open source tools
Author: by Valentina Palacín
Published at: Packt Publishing (February 12, 2021)
Get to grips with cyber threat intelligence and data-driven threat hunting while exploring expert tips and techniquesKey FeaturesSet up an environment to centralize all data in an Elasticsearch, Logstash, and Kibana (ELK) server that enables threat huntingCarry out atomic hunts to start the threat hunting process and understand the environmentPerform advanced hunting using MITRE ATT&CK Evals emulations and Mordor datasetsBook DescriptionThreat hunting (TH) provides cybersecurity analysts and enterprises with the opportunity to proactively defend themselves by getting ahead of threats before they can cause major damage to their business.
This book is not only an introduction for those who don’t know much about the cyber threat intelligence (CTI) and TH world, but also a guide for those with more advanced knowledge of other cybersecurity fields who are looking to implement a TH program from scratch.
You will start by exploring what threat intelligence is and how it can be used to detect and prevent cyber threats. As you progress, you’ll learn how to collect data, along with understanding it by developing data models. The book will also show you how to set up an environment for TH using open source tools.
8. 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
9. 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.
10. Deep Learning with PyTorch: Build, train, and tune neural networks using Python tools
Author: by Eli Stevens
Published at: Manning Publications; 1st edition (August 4, 2020)
We finally have the definitive treatise on PyTorch! It covers the basics and abstractions in great detail. I hope this book becomes your extended reference document. Soumith Chintala, co-creator of PyTorch Key Features Written by PyTorch’s creator and key contributors Develop deep learning models in a familiar Pythonic way Use PyTorch to build an image classifier for cancer detection Diagnose problems with your neural network and improve training with data augmentation Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications.
About The Book Every other day we hear about new ways to put deep learning to good use: improved medical imaging, accurate credit card fraud detection, long range weather forecasting, and more. PyTorch puts these superpowers in your hands. Instantly familiar to anyone who knows Python data tools like NumPy and Scikit-learn, PyTorch simplifies deep learning without sacrificing advanced features.
It’s great for building quick models, and it scales smoothly from laptop to enterprise. Deep Learning with PyTorch teaches you to create deep learning and neural network systems with PyTorch. This practical book gets you to work right away building a tumor image classifier from scratch.
11. Reinforcement Learning: Industrial Applications of Intelligent Agents
Author: by Phil Winder Ph. D.
Published at: O'Reilly Media; 1st edition (December 1, 2020)
Reinforcement learning (RL) will deliver one of the biggest breakthroughs in AI over the next decade, enabling algorithms to learn from their environment to achieve arbitrary goals. This exciting development avoids constraints found in traditional machine learning (ML) algorithms. This practical book shows data science and AI professionals how to learn by reinforcement and enable a machine to learn by itself.
Author Phil Winder of Winder Research covers everything from basic building blocks to state-of-the-art practices. You’ll explore the current state of RL, focus on industrial applications, learn numerous algorithms, and benefit from dedicated chapters on deploying RL solutions to production.
This is no cookbook; doesn’t shy away from math and expects familiarity with ML. Learn what RL is and how the algorithms help solve problemsBecome grounded in RL fundamentals including Markov decision processes, dynamic programming, and temporal difference learningDive deep into a range of value and policy gradient methodsApply advanced RL solutions such as meta learning, hierarchical learning, multi-agent, and imitation learningUnderstand cutting-edge deep RL algorithms including Rainbow, PPO, TD3, SAC, and moreGet practical examples through the accompanying website
12. Machine Learning and Data Science Blueprints for Finance: From Building Trading Strategies to Robo-Advisors Using Python
Author: by Hariom Tatsat
Published at: O'Reilly Media; 1st edition (December 1, 2020)
Over the next few decades, machine learning and data science will transform the finance industry. With this practical book, analysts, traders, researchers, and developers will learn how to build machine learning algorithms crucial to the industry. You’ll examine ML concepts and over 20 case studies in supervised, unsupervised, and reinforcement learning, along with natural language processing (NLP).
Ideal for professionals working at hedge funds, investment and retail banks, and fintech firms, this book also delves deep into portfolio management, algorithmic trading, derivative pricing, fraud detection, asset price prediction, sentiment analysis, and chatbot development. You’ll explore real-life problems faced by practitioners and learn scientifically sound solutions supported by code and examples.
This book covers:Supervised learning regression-based models for trading strategies, derivative pricing, and portfolio managementSupervised learning classification-based models for credit default risk prediction, fraud detection, and trading strategiesDimensionality reduction techniques with case studies in portfolio management, trading strategy, and yield curve constructionAlgorithms and clustering techniques for finding similar objects, with case studies in trading strategies and portfolio managementReinforcement learning models and techniques used for building trading strategies, derivatives hedging, and portfolio managementNLP techniques using Python libraries such as NLTK and scikit-learn for transforming text into meaningful representations
13. Machine Learning For Absolute Beginners: A Plain English Introduction (Machine Learning from Scratch)
Author: by Oliver Theobald
Published at: Independently published (January 1, 2018)
Featured by Tableau as the first of “7 Books About Machine Learning for Beginners” Ready to crank up a virtual server and smash through petabytes of data? Want to add ‘Machine Learning’ to your LinkedIn profile? Well, hold on there… Before you embark on your epic journey, there are some theory and statistical principles to weave through first.
But rather than spend $30-$50 USD on a dense long textbook, you may want to read this book first. As a clear and concise alternative to a textbook, this book provides a practical and high-level introduction to machine learning. Machine Learning for Absolute Beginners Second Edition has been written and designed for absolute beginners.
This means plain-English explanations and no coding experience required. Where core algorithms are introduced, clear explanations and visual examples are added to make it easy and engaging to follow along at home. This major new edition features many topics not covered in the First Edition, including Cross Validation, Data Scrubbing and Ensemble Modeling.
14. Introduction to the Theory of Computation
Author: by Michael Sipser
Published at: Cengage Learning; 3rd edition (June 27, 2012)
Gain a clear understanding of even the most complex, highly theoretical computational theory topics in the approachable presentation found only in the market-leading INTRODUCTION TO THE THEORY OF COMPUTATION, 3E. The number one choice for today’s computational theory course, this revision continues the book’s well-know, approachable style with timely revisions, additional practice, and more memorable examples in key areas.
A new first-of-its-kind theoretical treatment of deterministic context-free languages is ideal for a better understanding of parsing and LR(k) grammars. You gain a solid understanding of the fundamental mathematical properties of computer hardware, software, and applications with a blend of practical and philosophical coverage and mathematical treatments, including advanced theorems and proofs.
INTRODUCTION TO THE THEORY OF COMPUTATION, 3E’s comprehensive coverage makes this a valuable reference for your continued studies in theoretical computing.
15. Building Machine Learning Pipelines: Automating Model Life Cycles with TensorFlow
Author: by Hannes Hapke
Published at: O'Reilly Media; 1st edition (August 4, 2020)
Companies are spending billions on machine learning projects, but it’s money wasted if the models can’t be deployed effectively. In this practical guide, Hannes Hapke and Catherine Nelson walk you through the steps of automating a machine learning pipeline using the TensorFlow ecosystem.
You’ll learn the techniques and tools that will cut deployment time from days to minutes, so that you can focus on developing new models rather than maintaining legacy systems. Data scientists, machine learning engineers, and DevOps engineers will discover how to go beyond model development to successfully productize their data science projects, while managers will better understand the role they play in helping to accelerate these projects.
Understand the steps that make up a machine learning pipelineBuild your pipeline using components from TensorFlow ExtendedOrchestrate your machine learning pipeline with Apache Beam, Apache Airflow and Kubeflow PipelinesWork with data using TensorFlow Data Validation and TensorFlow TransformAnalyze a model in detail using TensorFlow Model AnalysisExamine fairness and bias in your model performanceDeploy models with TensorFlow Serving or convert them to TensorFlow Lite for mobile devicesUnderstand privacy-preserving machine learning techniques
16. The Master Algorithm: How the Quest for the Ultimate Learning Machine Will Remake Our World
Author: by Pedro Domingos
Published at: Basic Books; Reprint edition (February 13, 2018)
A thought-provoking and wide-ranging exploration of machine learning and the race to build computer intelligences as flexible as our ownIn the world’s top research labs and universities, the race is on to invent the ultimate learning algorithm: one capable of discovering any knowledge from data, and doing anything we want, before we even ask.
In The Master Algorithm, Pedro Domingos lifts the veil to give us a peek inside the learning machines that power Google, Amazon, and your smartphone. He assembles a blueprint for the future universal learner-the Master Algorithm-and discusses what it will mean for business, science, and society.
If data-ism is today’s philosophy, this book is its bible.