Kissinger, former Secretary of State in the Nixon & Ford administrations; Schmidt, former CEO of Google; Huttenlocher, an acclaimed computer vision research who is currently a dean at MIT's Schwarzman College of Computing. Why it's good: The book's trio of authors brings their expertise in statecraft, business, and academia to explore how AI is set to reshape society. "AI's promise of epoch-making transformations--in society, economics, politics, and foreign policy--portends effects beyond the scope of any single author's or field's traditional focuses," the authors contend in an online preview of the book. In the time since The Age of A.I. was published, a revolution in generative AI has brought us much closer to the promise of artificial general intelligence -- the representation of human cognitive abilities in software-- making the book's discussion of how society will change as machines increasingly perform human tasks all the more relevant.

In 2017, the Google Brain team introduced Transformers in a paper called, Attention is all you need. They proposed a new simple network architecture based solely on attention mechanisms, dispensing with recurrence and convolutions entirely. Today, transformers have become the model of choice for NLP and computer vision. The Transformer architecture implements an encoder-decoder structure without recurrence and convolutions. Taking cognizance of the importance of Transformers in machine learning and AI, we have listed five books to understand the sequence transduction model better.

Numerous books provide in-depth examinations of artificial intelligence's core concepts, technical processes, and applications. This list covers books written by eminent computer scientists and practitioners with deep ties to the artificial intelligence business. So, whether you're a researcher, an engineer, or a business professional working in the AI/ML space, you're sure to find a few new titles to add to your reading list! A leading artificial intelligence (AI) researcher and entrepreneur debunks the illusion that superintelligence is just a few clicks away and argues that this myth impedes innovation and distorts our capacity to make the critical next jump. According to futurists, AI will soon surpass the capabilities of the most gifted human mind.

At the start of your journey into ML, you should prepare and build all the skills needed to make full use of the tools needed in Machine Learning itself. While the journeys differ, it's fair to say that Python is a stable in this area. To make the most use of Machine Learning frameworks, it's also crucial to learn data processing and data analysis skills. So where can you learn all of this? The cookbook series of O'Reilly is always a great starting point for any language.

One of my 2019 new year resolutions was to read more books. I signed up for GoodReads and stated a reading challenge with a modest goal of finishing twelve books. I finished twenty-four, and many of these books are great reads for entrepreneurs, CIOs, and other digital transformation leaders. What makes a good book? My favorites have a strong focus, come from a unique perspective, are easy to read/listen, deliver a clear message, back their hypotheses with data or testimonials, tell personal stories, and are concise.

Natural Language Processing, or NLP for short, is the study of computational methods for working with speech and text data. The field is dominated by the statistical paradigm and machine learning methods are used for developing predictive models. In this post, you will discover the top books that you can read to get started with natural language processing. As practitioners, we do not always have to grab for a textbook when getting started on a new topic. Code examples in the book are in the Python programming language.

Books are a fantastic investment. You get years of experience for tens of dollars. I love books and I read every machine learning book I can get my hands on. I think having good references is the fastest way to getting good answers to your machine learning questions, and having multiple books can give you multiple perspectives on tough questions. In this guide, you will discover the top books on machine learning.