Original article was published by AsianMarketCap Official on Artificial Intelligence on Medium
Suggested Artificial Intelligence Books for 2020
Artificial Intelligence (AI) influences almost every facet of our lives to help improve efficiencies and augment human limitations. It is intertwined in almost all that we do and it’s quite hard to imagine living life without it.
Our world looks vastly different in every way because of this disruptive technology. We can expect AI to become even more entangled into our daily lives, workplaces and society.
To give more in-depth learning about AI, we will be sharing with you 5 top Artificial Intelligence books that are shelf-worthy for 2020.
1. You Look Like a Thing and I Love You: How AI Works and Why It’s Making the World a Weirder Place by Janelle Shane
This book uses cartoons and humorous pop-culture experiments to look inside the minds of the algorithms that run our world, making AI and Machine Learning both accessible and entertaining. It was published on November 2019.
The author delivers the answers to every AI question you’ve ever asked and some you definitely haven’t. In this smart, often hilarious introduction to the most interesting science of our time, Shane shows how these programs learn, fail and adapt and how they reflect the best and worst of humanity.
“I can’t think of a better way to learn about Artificial Intelligence and I’ve never had so much fun along the way.” — Adam Grant, New York Times best-selling author of Originals
2. Deep Learning (Adaptive Computation and Machine Learning Series) by Ian Goodfellow, Yoshua Bengio and Aaron Courville
This book is 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. It was published on 2015.
Deep 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.
The text offers mathematical and conceptual background, covering relevant concepts in linear algebra, probability theory and information theory, numerical computation, and machine learning. It describes deep learning techniques used by practitioners in industry, including deep feedforward networks, regularization, optimization algorithms, convolutional networks, sequence modeling, and practical methodology; and it surveys such applications as natural language processing, speech recognition, computer vision, online recommendation systems, bioinformatics, and video games.
“Written by three experts in the field, Deep Learning is the only comprehensive book on the subject.” — Elon Musk, co-chairman of Open AI; co-founder and CEO of Tesla and SpaceX
3. AI for People and Business: A Framework for Better Human Experiences and Business Success by Alex Castrounis
This book was written for executives, managers and non-technical folks who are interested in leveraging Artificial Intelligence within their organization and to fill the gap in the AI literature. It was published on July 2019.
It’s becoming imperative for business leaders to understand artificial intelligence and machine learning at an appropriate level in order to build great data-centric products and solutions. AI is hard to simplify because it’s inherently not simple. But it’s all about what level of granularity is right for what target audience. This book really simplifies all those very complex things in ways that benefit executives and managers.
“A must read for business executives and managers interested in learning about AI and unlocking its benefits. Alex Castrounis has simplified complex topics so that anyone can begin to leverage AI within their organization.” — Dan Park, GM & Director, Uber
4. Machine Learning Yearning by Andrew Ng
This is a great book for practitioners. It has a broad coverage of machine learning and its application to AI. But it’s written in a much more how-to- or cookbook-style approach than that book. It’s sort of like, if you want to do this, this is how you do it; if you want to do that, this is how you do that. It’s also written in a very logical order that closely mimics the process, key considerations and trade-offs that data scientists and machine learning engineers follow when working on machine learning projects, end to end.
“Andrew Ng is giving practical advice to the ML engineer through his experience at Google Brian, Baidu and teaching (Stanford and Coursera). It’s a light technical book, giving succinct technical advice from someone being in the field…rules of thumbs, tricks, layman advice from lots of practice, trial and error.” — Precia Carraway
5. The Master Algorithm: How the Quest for the Ultimate Learning Machine Will Remake Our World by Pedro Domingos
Machine learning is the automation of discovery — the scientific method on steroids — that enables intelligent robots and computers to program themselves. No field of science today is more important yet more shrouded in mystery.
Pedro Domingos, one of the field’s leading lights, lifts the veil for the first time to give us a peek inside the learning machines that power Google, Amazon, and your smartphone. He charts a course through machine learning’s five major schools of thought, showing how they turn ideas from neuroscience, evolution, psychology, physics, and statistics into algorithms ready to serve you. Step by step, he assembles a blueprint for the future universal learner — the Master Algorithm — and discusses what it means for you, and for the future of business, science, and society.
The Master Algorithm is the essential guide for anyone and everyone wanting to understand not just how the revolution will happen, but how to be at its forefront.
There are quite a number of quality reads about Artificial Intelligence. We do hope these suggested books can help you at least get a glimpse of the vast world this technological advancement has been evolving through time.
Have fun reading!