Best Book On Artificial Intelligence
We’re obviously deeply interested in AI and as a team have gone through a long list of books on the topic. Some of us were beginners at the start, others were already experts. Here is a shortlist that reflects our collective recommendations, but we’ve highlighted who we think should find the particular book most interesting so that you can zero in on the one that’s best for you. Please feel free to add to the list in the comments section with your favorite recommendations, as we’ll keep reading and updating this list on a regular basis.
Introduction to Artificial Intelligence
Originally written over 40 years ago, and released as a second edition in 1985, this classic provides an introduction to the science of reasoning processes in computers, as well as the approaches and results of more than two decades of research. Subjects such as, proving predicate-calculus theorem, machine architecture, psychological simulation, automatic programming, novel software techniques, industrial automation, have been enhanced by diagrams and clear illustrations.
Who would find this book most interesting:
Anyone who is entering the Artificial Intelligence space and would like to have a much deeper understanding of the field. Especially if you would like explore new topics and develop a broad understanding of different areas, so that you will know what to learn next.
Deep Learning (Adaptive Computation and Machine Learning series)
After two and a half years in the making, Deep Learning (Adaptive Computation and Machine Learning series) was released in late 2016 and has quickly become a groundbreaking resource on the subject of deep learning. Written by three of the top academics in the subject of deep learning, this book has been created for both graduate-level university students studying computer science, and software engineers alike. The authors have tackled the subject head-on, while also providing a necessary framework for understanding such highly technical subjects as convolution, generative models, and hidden layers.
Who would find this book most interesting:
Experienced engineers who want to get serious about Deep Learning. This is a great resource before you start coding with any framework so that it is easier to really understand and get going faster.
The Elements of Statistical Learning: Data Mining, Inference, and Prediction, Second Edition (Springer Series in Statistics)
This book is an excellent resource for anyone looking for a better understanding of the concepts of data mining, machine learning, and bioinformatics through a statistical approach. It is quite comprehensive, with many topics including neural networks, support vector machines, classification trees and boosting. Concepts are well defined and clearly presented with vivid color illustrations throughout the book.
Who would find this book most interesting:
Our opinion is that this is advanced stuff. Of course academics and statisticians will dig it, as well as anyone technical that needs to beef up their knowledge of the topic. When you read the published reviews, it says that it is relevant for “anyone interested in the field…as an entry point to the area…” so what do we know?
Python Machine Learning
This very practical guide offers deep insights into machine learning, as well as a hands-on approach to the latest developments in predictive analytics. Python Machine Learning covers a wide range of powerful Python libraries, including scikit-learn, Theano, and Pylearn2, and features guidance and tips on everything from sentiment analysis to neural networks. Sebastian Raschka has provided a crucial resource that clearly demonstrates what makes Python one of the leading data science languages in the world.
Who would find this book most interesting:
Written for anyone looking to ask better questions of their data, or for those who need to improve and extend the capabilities of their machine learning systems. If you are a beginner in machine learning, this book is also for you, but every reader should at least have a solid foundation in Python.
How to Create a Mind: The Secret of Human Thought Revealed
Written by acclaimed futurist Ray Kurzweil, this book takes a deep dive into how future civilizations will be dominated by the interconnectedness of humans and machines. He describes the rise and development of intelligent machines through the process of reverse engineering the human brain. Ray describes this process through clear explanations of themes such as logical agents, the quantification of uncertainty, learning from example, the communication, perception, and action of natural language processing, and more. The book concludes with a discussion of the philosophical foundations of A.I., as well as an examination of what lies ahead in the years to come.
Who would find this book most interesting:
Ideal for those with an interest in the future of advanced machine learning, with a focus on the correlation between intelligent machines and humanity. If you are trying to calibrate yourself, don’t worry, this one is for everyone, the mere fact that you are reading this should tell you that this book is accessible, think of it as a philosophy treatise as opposed to a technical manual.
Artificial Intelligence and Soft Computing: Behavioral and Cognitive Modeling of the Human Brain
Widely considered to be the bible of theoretical A.I.within the field of computer science, this book provides a comprehensive resource that is both conceptually advanced and accessible enough to enable the reader to both understand and apply modern and traditional A.I.concepts. The content is diverse, but complete, covering subjects ranging from the behavioral perspective of human cognition to non-monotonic and spatio-temporal reasoning. The text is clearly written, practical, and thorough.
Who would find this book most interesting:
This book should have broad appeal: it provides an excellent resource for anyone involved in computer science— from students to seasoned professionals.
Reinforcement Learning: An Introduction
Reinforcement learning has quickly become one of the hottest topics in Artificial Intelligence research today. This book provides a comprehensive introduction to many of the key insights and algorithms associated with reinforcement learning. The text is intelligibly written into 3 sections with the first section dedicated to a deeper understanding of the Markov decision processes. The second section covers basic solution methods such as dynamic programming, Monte Carlo methods, and temporal-difference learning. Lastly, the third section provides a unified view of the solution methodology covering topics that range from artificial neural networks to eligibility traces, and planning. As a whole, Richard S. Sutton and Andrew G. Barto do an excellent job of covering both the conceptual foundations of reinforcement learning, as well as its latest developments and applications.
Who would find this book most interesting:
It’s an introductory book to a new field of Artificial Intelligence. Engineers who are looking to stay on top of the latest trends in artificial intelligence, including a thorough understanding of reinforcement learning, should find this book helpful.
I hope you enjoyed our list of recommendations. Don’t forget to leave your recommendations in the comment section below.
