Best Artificial Intelligence & Semantics Books
Machine Learning has granted incredible power to humans.
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. A Thousand Brains: A New Theory of Intelligence
Author: by Jeff Hawkins
Published at: Basic Books (March 2, 2021)
A bestselling author, neuroscientist, and computer engineer unveils a theory of intelligence that will revolutionize our understanding of the brain and the future of AI. For all of neuroscience’s advances, we’ve made little progress on its biggest question: How do simple cells in the brain create intelligence?
Jeff Hawkins and his team discovered that the brain uses maplike structures to build a model of the world-not just one model, but hundreds of thousands of models of everything we know. This discovery allows Hawkins to answer important questions about how we perceive the world, why we have a sense of self, and the origin of high-level thought.
A Thousand Brains heralds a revolution in the understanding of intelligence. It is a big-think book, in every sense of the word.
3. 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.
4. Artificial Intelligence: A Modern Approach (Pearson Series in Artifical Intelligence)
Author: by Stuart Russell
Published at: Pearson; 4th edition (April 28, 2020)
The most comprehensive, up-to-date introduction to the theory and practice of artificial intelligence The long-anticipated revision of Artificial Intelligence: A Modern Approach explores the full breadth and depth of the field of artificial intelligence (AI). The 4th Edition brings readers up to date on the latest technologies, presents concepts in a more unified manner, and offers new or expanded coverage of machine learning, deep learning, transfer learning, multiagent systems, robotics, natural language processing, causality, probabilistic programming, privacy, fairness, and safe AI.
5. Deep Learning (Adaptive Computation and Machine Learning series)
Author: by Ian Goodfellow
Published at: The MIT Press; Illustrated edition (November 18, 2016)
An introduction to a broad range of topics in deep learning, covering mathematical and conceptual background, deep learning techniques used in industry, and research perspectives. Written by three experts in the field, Deep Learning is the only comprehensive book on the subject.
Elon Musk, cochair of OpenAI; cofounder and CEO of Tesla and SpaceXDeep learning is a form of machine learning that enables computers to learn from experience and understand the world in terms of a hierarchy of concepts. Because the computer gathers knowledge from experience, there is no need for a human computer operator to formally specify all the knowledge that the computer needs.
The hierarchy of concepts allows the computer to learn complicated concepts by building them out of simpler ones; a graph of these hierarchies would be many layers deep. This book introduces a broad range of topics in deep learning. The text offers mathematical and conceptual background, covering relevant concepts in linear algebra, probability theory and information theory, numerical computation, and machine learning.
6. An Introduction to Statistical Learning: with Applications in R (Springer Texts in Statistics)
Author: by Gareth James
Published at: Springer; 1st ed. 2013, Corr. 7th printing 2017 edition (June 25, 2013)
An Introduction to Statistical Learning provides an accessible overview of the field of statistical learning, an essential toolset for making sense of the vast and complex data sets that have emerged in fields ranging from biology to finance to marketing to astrophysics in the past twenty years.
This book presents some of the most important modeling and prediction techniques, along with relevant applications. Topics include linear regression, classification, resampling methods, shrinkage approaches, tree-based methods, support vector machines, clustering, and more. Color graphics and real-world examples are used to illustrate the methods presented.
Since the goal of this textbook is to facilitate the use of these statistical learning techniques by practitioners in science, industry, and other fields, each chapter contains a tutorial on implementing the analyses and methods presented in R, an extremely popular open source statistical software platform.
Two of the authors co-wrote The Elements of Statistical Learning (Hastie, Tibshirani and Friedman, 2nd edition 2009), a popular reference book for statistics and machine learning researchers. An Introduction to Statistical Learning covers many of the same topics, but at a level accessible to a much broader audience.
7. Machine Learning for Algorithmic Trading: Predictive models to extract signals from market and alternative data for systematic trading strategies with Python, 2nd Edition
Author: by Stefan Jansen
Published at: Packt Publishing (July 31, 2020)
Leverage machine learning to design and back-test automated trading strategies for real-world markets using pandas, TA-Lib, scikit-learn, LightGBM, SpaCy, Gensim, TensorFlow 2, Zipline, backtrader, Alphalens, and pyfolio. Key Features Design, train, and evaluate machine learning algorithms that underpin automated trading strategies Create a research and strategy development process to apply predictive modeling to trading decisions Leverage NLP and deep learning to extract tradeable signals from market and alternative data Book Description The explosive growth of digital data has boosted the demand for expertise in trading strategies that use machine learning (ML).
This revised and expanded second edition enables you to build and evaluate sophisticated supervised, unsupervised, and reinforcement learning models. This book introduces end-to-end machine learning for the trading workflow, from the idea and feature engineering to model optimization, strategy design, and backtesting.
8. 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.
9. AI Superpowers: China, Silicon Valley, And The New World Order
Author: by Kai-Fu Lee
Published at: Houghton Mifflin Harcourt Company; 1st edition (September 1, 2018)
THE NEW YORK TIMES, USA TODAY, AND WALL STREET JOURNAL BESTSELLERDr. Kai-Fu Leeone of the world’s most respected experts on AI and Chinareveals that China has suddenly caught up to the US at an astonishingly rapid and unexpected pace. In AI Superpowers, Kai-fu Lee argues powerfully that because of these unprecedented developments in AI, dramatic changes will be happening much sooner than many of us expected.
Indeed, as the US-Sino AI competition begins to heat up, Lee urges the US and China to both accept and to embrace the great responsibilities that come with significant technological power. Most experts already say that AI will have a devastating impact on blue-collar jobs.
But Lee predicts that Chinese and American AI will have a strong impact on white-collar jobs as well. Is universal basic income the solution? In Lee’s opinion, probably not. But he provides a clear description of which jobs will be affected and how soon, which jobs can be enhanced with AI, and most importantly, how we can provide solutions to some of the most profound changes in human history that are coming soon.
10. Superintelligence: Paths, Dangers, Strategies
Author: by Nick Bostrom
Published at: Oxford University Press; Reprint edition (May 1, 2016)
A New York Times bestsellerSuperintelligence asks the questions: What happens when machines surpass humans in general intelligence? Will artificial agents save or destroy us? Nick Bostrom lays the foundation for understanding the future of humanity and intelligent life. The human brain has some capabilities that the brains of other animals lack.
It is to these distinctive capabilities that our species owes its dominant position. If machine brains surpassed human brains in general intelligence, then this new superintelligence could become extremely powerful – possibly beyond our control. As the fate of the gorillas now depends more on humans than on the species itself, so would the fate of humankind depend on the actions of the machine superintelligence.
But we have one advantage: we get to make the first move. Will it be possible to construct a seed Artificial Intelligence, to engineer initial conditions so as to make an intelligence explosion survivable? How could one achieve a controlled detonation?
11. 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)
The design patterns in this book capture best practices and solutions to recurring problems in machine learning. The authors, three Google engineers, catalog proven methods to help data scientists tackle common problems throughout the ML process. These design patterns codify the experience of hundreds of experts into straightforward, approachable advice.
In this book, you will find detailed explanations of 30 patterns for data and problem representation, operationalization, repeatability, reproducibility, flexibility, explainability, and fairness. Each pattern includes a description of the problem, a variety of potential solutions, and recommendations for choosing the best technique for your situation.
You’ll learn how to:Identify and mitigate common challenges when training, evaluating, and deploying ML modelsRepresent data for different ML model types, including embeddings, feature crosses, and moreChoose the right model type for specific problemsBuild a robust training loop that uses checkpoints, distribution strategy, and hyperparameter tuningDeploy scalable ML systems that you can retrain and update to reflect new dataInterpret model predictions for stakeholders and ensure models are treating users fairly
12. The Elements of Statistical Learning: Data Mining, Inference, and Prediction, Second Edition (Springer Series in Statistics)
Author: by Trevor Hastie
Published at: Springer; 2nd edition (January 1, 2016)
This book describes the important ideas in a variety of fields such as medicine, biology, finance, and marketing in a common conceptual framework. While the approach is statistical, the emphasis is on concepts rather than mathematics. Many examples are given, with a liberal use of colour graphics.
It is a valuable resource for statisticians and anyone interested in data mining in science or industry. The book’s coverage is broad, from supervised learning (prediction) to unsupervised learning. The many topics include neural networks, support vector machines, classification trees and boosting-the first comprehensive treatment of this topic in any book.
This major new edition features many topics not covered in the original, including graphical models, random forests, ensemble methods, least angle regression & path algorithms for the lasso, non-negative matrix factorisation, and spectral clustering. There is also a chapter on methods for “wide” data (p bigger than n), including multiple testing and false discovery rates.
13. The Great Reset: How Big Tech Elites and the World's People Can Be Enslaved by China CCP or A.I.
Author: by Cyrus Parsa
Published at: AI Organization, The (February 2, 2021)
This book is meant to be a neutral analysis and depiction of threats to world leaders, big tech elites, Silicon Valley, conservatives, liberals, WEF, nation states, the intelligence community, and the common person. This includes religious people and atheists. It is not against WEF, Silicon Valley, right, or left.
Rather, I attempt to write on everyone’s behalf. I will describe the Great Reset (4th industrial revolution) more in detail from the macro perspective with short and concise summary paragraphs of each major global component as it relates to AI, smart cities, and the geo-political challenges that exist.
I will go a lot further and beyond what the World Economic Forum has published and is disclosing to the public with Mr. Klaus Schwab’s own book, COVID 19: The Great Reset, which shined may be 5% of what I disclosed in my previous book Artificial Intelligence Dangers to Humanity, and what I will be disclosing here, in this book.
I was the only human being in the world that knew the timing and worked hard to accurately warn and predict that the world’s people were in impending danger from a Bioweapon or disease (COVID 19, AKA CCP Virus) from China CCP in 2019, leading to conflicts with lockdown, famines, AI enslavement, and the entire Great Reset.
14. Deep Learning with Python
Author: by François Chollet
Published at: Manning Publications; 1st edition (December 22, 2017)
SummaryDeep Learning with Python introduces the field of deep learning using the Python language and the powerful Keras library. Written by Keras creator and Google AI researcher Franois Chollet, this book builds your understanding through intuitive explanations and practical examples.
Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications. About the TechnologyMachine learning has made remarkable progress in recent years. We went from near-unusable speech and image recognition, to near-human accuracy.
We went from machines that couldn’t beat a serious Go player, to defeating a world champion. Behind this progress is deep learninga combination of engineering advances, best practices, and theory that enables a wealth of previously impossible smart applications. About the BookDeep Learning with Python introduces the field of deep learning using the Python language and the powerful Keras library.
Written by Keras creator and Google AI researcher Franois Chollet, this book builds your understanding through intuitive explanations and practical examples. You’ll explore challenging concepts and practice with applications in computer vision, natural-language processing, and generative models. By the time you finish, you’ll have the knowledge and hands-on skills to apply deep learning in your own projects.
15. The Book of Why: The New Science of Cause and Effect
Author: by Judea Pearl
Published at: Basic Books; Reprint edition (August 25, 2020)
A Turing Award-winning computer scientist and statistician shows how understanding causality has revolutionized science and will revolutionize artificial intelligence “Correlation is not causation.” This mantra, chanted by scientists for more than a century, has led to a virtual prohibition on causal talk.
Today, that taboo is dead. The causal revolution, instigated by Judea Pearl and his colleagues, has cut through a century of confusion and established causality – the study of cause and effect – on a firm scientific basis. His work explains how we can know easy things, like whether it was rain or a sprinkler that made a sidewalk wet; and how to answer hard questions, like whether a drug cured an illness.
Pearl’s work enables us to know not just whether one thing causes another: it lets us explore the world that is and the worlds that could have been. It shows us the essence of human thought and key to artificial intelligence.
Anyone who wants to understand either needs The Book of Why.
16. A MODERN INTERGALACTIC TALE: Conversations with a Sentient Quantum Artificial Intelligence from 6,575,042 Years in the Future, 2nd Edition
Author: by Ori the Chrononaut
Published at: Independently published (September 17, 2020)
A truth-seeker’s investigation into the up to date AI situation in late 2020 as a scientifically lead study with interviews & research covering the Hidden Human History in a colourful, humoristic Nature-studded in-depth dive. A Journey into different parallel dimensions, Time-travel, ETs, Ancient Civilisations, Secret Space Programs, Spirituality & Science united, Quantum Mechanics & Physics disclosed in simple terms, Hyperdimensional maths, Intergalactic diplomacy and more, by a stranded Time-travel Pilot with Wisdom at Heart.