AI Books

In this practical guide for business leaders, Kavita Ganesan takes the mystery out of implementing AI, showing you how to launch AI initiatives that get results. With real-world AI examples to spark your own ideas, you’ll learn how to identify high-impact AI opportunities, prepare for AI transitions, and measure your AI performance.

This book provides a comprehensive guide on how to utilize the OpenAI API with ease. It explores imaginative methods of utilizing this tool for your specific needs and showcases successful businesses that have been established through its use.

Complexity is reduced thanks to easy step-by-step guidance so that you can progress easily with the Python language even if you have never programmed before.

lets you in on the ground floor, covering the entry-level materials that will get you up and running building models you need to perform practical tasks. It takes a look at the underlying—and fascinating—math principles that power machine learning but also shows that you don't need to be a math whiz to build fun new tools and apply them to your work and study

This e-book is designed for absolute beginners, so no programming experience is required. However, two of the later chapters introduce Python to demonstrate an actual machine learning model, so you will see some programming used in this book.

Through a recent series of 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.

covers all the major machine learning approaches, ranging from classical linear and logistic regression, through to modern support vector machines, deep learning, boosting, and random forests. There is also no shortage of details on the various approaches and the interested reader can gain further information on any particular method via the innovative companion book wiki. The book does not assume any high level mathematical or statistical training or even programming experience, so should be accessible to almost anyone willing to invest the time to learn about these methods. It should certainly be required reading for anyone starting a PhD program in this area and will serve as a useful reference as they progress further.

The Singularity Is Near portrays what life will be like after this event—a human-machine civilization where our experiences shift from real reality to virtual reality and where our intelligence becomes nonbiological and trillions of times more powerful than unaided human intelligence. In practical terms, this means that human aging and pollution will be reversed, world hunger will be solved, and our bodies and environment transformed by nanotechnology to overcome the limitations of biology, including death.

how we think, Kahneman reveals where we can and cannot trust our intuitions and how we can tap into the benefits of slow thinking. He offers practical and enlightening insights into how choices are made in both our business and our personal lives―and how we can use different techniques to guard against the mental glitches that often get us into trouble.

This profoundly ambitious and original book breaks down a vast track of difficult intellectual terrain. After an utterly engrossing journey that takes us to the frontiers of thinking about the human condition and the future of intelligent life, we find in Nick Bostrom's work nothing less than a reconceptualization of the essential task of our time.

A thought-provoking and wide-ranging exploration of machine learning and the race to build computer intelligences as flexible as our own In the world's top research labs and universities, the race is on to invent the ultimate learning algorithm

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.

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.