The best books on artificial intelligence
Here we have listed some basic and advanced AI books to help you navigate AI.
Written by Chris Baker
This book explores the potential implications of artificial intelligence and how it will impact the world in the coming years. It talks about how AI seeks to help human cognitive limitations. It covers:
- A Brief History of Artificial Intelligence
- The State of Machine Learning Artificial neural networks applied to machine learning
- How to create an AI-ready culture
- The impact of AI on our daily lives
Written by Tom Towley
This book gives you a fundamental understanding of artificial intelligence and its impact. It provides a non-technical introduction to important concepts such as machine learning, deep learning, natural language processing, robotics, and more. The author goes on to explore issues related to the future impact of AI on aspects such as social trends, ethics, governments, company structures, and everyday life.
Written by Neil Wilkins
Have you ever wondered what’s going on with artificial intelligence and intelligent machines? Are they going to decide cases like the DMCA and copyright infringement in a few years? Will they eventually become so smart that they can fully operate self-driving vehicles?
What impact will these intelligent machines have on humanity and society? How will they affect the workings of humans–essentially, will AI take over?
This book will introduce you to the basic concepts of artificial intelligence, as well as many other topics related to AI and machine learning. It even addresses issues such as AI and employment, as well as AI and ethics. However, when it comes to really learning about the subject, this book doesn’t provide as much technical knowledge. Those who want to know how to do it may want to look elsewhere.
Author – Chandra S.S.V.
This book is primarily intended for undergraduate and graduate students of computer science and engineering. This textbook covers the gap between the complex contexts of artificial intelligence and machine learning. It presents the greatest number of case studies and worked examples. In addition to artificial intelligence and machine learning, it also covers different types of learning such as reinforcement, supervised, unsupervised, and statistical learning. It presents well-explained algorithms and pseudocodes for each topic, making this book very useful for students.
Written by Rahul Kumar, Ankit Dixit, Denis Rothman, Amir Ziai, Matthew Lamons
This book will help you get contextualized in the real world with deep learning problems related to research and applications. Develop and implement machine intelligence using real-world examples based on AI. This book offers knowledge of machine learning, deep learning, data analysis, TensorFlow, Python, and the basics of artificial intelligence, and will allow you to apply your skills to real-world projects.
Written by Deepak Hemani
This book takes a bottom-up approach, looking at the basic strategies needed to solve problems, mostly in the intelligence part. Its main features include an introductory course on artificial intelligence, a knowledge-based approach using agents around the world, and detailed, well-structured algorithms with proofs.
Written by Dr. Dhiraj Mehrotra
This book provides an introduction to artificial intelligence and machine learning with an improved technological framework.
Written by Max Tegmark
This book introduces readers to the latest AI thought process to explore the next stage of human existence. Here the author explores the burning questions of how to succeed through automation without leaving humans out of work, how to ensure that future AI systems work as intended without crashing or hacking, and how to thrive with AI without ultimately being outsmarted by deadly autonomous machines.
By Stuart Russell and Peter Norvig
This edition covers changes and developments in artificial intelligence since they were covered in the last edition of this book in 2003. This book covers the latest developments in artificial intelligence in practical speech recognition, machine translation, autonomous vehicles, and consumer robotics. It also covers advances in areas such as probabilistic reasoning, machine learning, and computer vision.
By Giuseppe Bonaccorso, Armando Fandango, Rajalingappaa Shanmugamani
This book is a complete guide to learning popular machine learning algorithms. You will learn how to extract functions from your dataset and perform dimensionality reduction using Python-based libraries. Then you’ll learn about the advanced features of Tensorflow and how to implement various methods related to object classification, object detection, image segmentation, and more. By the end of this book, you will have an in-depth knowledge of Tensorflow and become an expert in AI problem solving.
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