Dr. Pablo Rivas - Deep Learning for Beginners: A Beginners Guide to Getting Up and Running with Deep Learning from Scratch Using Python

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I have known and worked with Dr. Pablo Rivas for more than 5 years. He is one of the leading experts in deep learning and artificial intelligence ethics. In this book, he takes you on a learning journey that aims to bring you up to speed with the latest ideas through a hands-on practical approach to deep learning. In the last few years, deep learning has experienced breakthroughs that have transformed several communities around the world both positively and negatively. It is imperative that deep learning education includes discussions of the societal implication of certain algorithms so that learners and practitioners can have awareness of the tremendous positive potential of deep learning-based technology as well as its possible negative consequences. Dr. Rivas has continued to evolve as a machine learning scientist and educator to meet these needs by educating students and sharing his research through papers that are being read around the world. I have had the privilege of working with him in a study that warns about the impact and repercussions of artificial intelligence being developed, funded, and adopted in only a few places in the world. However, this book stands as an invitation to anyone anywhere in the world to jump in and start learning about deep learning so that more people can have access to this type of specialized knowledge.

Dr. Rivas has done a great job of explaining concepts with practical examples, interesting applications, and ethics discussions. He has made use of Google Colabs, which makes deep learning tools and libraries accessible to anyone who does not have a high-performance computer, enabling them to run the code on the cloud. Further, he has used his skills as a certified online instructor and teacher to convey ideas in a way that is memorable and making thought-provoking questions that will leave you thinking beyond what seems to be obvious. By reading this book, you will be part of an education movement that increases access to resources in artificial intelligence engineering and improves awareness of the long and short term effects of artificial intelligence.

This book will serve you well in your learning journey by providing you with several examples, best practices, and fully working code snippets that will give you the understanding you need to apply deep learning in several disciplines that include computer vision, natural language processing, learning representations and more. The way the book is organized will give you a smooth transition between supervised and unsupervised models that can accelerate your grasp of knowledge and easier navigation between topics if you need to move at a faster pace.

In Deep Learning for Beginners, Dr. Rivas encapsulates the knowledge gained through years as a world-class machine learning scientist, an educator, a community leader, and a passionate advocate for underrepresented groups in artificial intelligence. With his words, step-by-step instructions, source code snippets, examples, professional tips, and additional information, you will learn how to continuously enhance your skills and grow professionally.

Become a deep learning practitioner, professional, or scientist by reading this book and applying these state of the art techniques today; your journey starts here.

Laura Montoya

Published Author, Speaker
Founder & Executive Director
Accel.AI Institute

Dr. Pablo Rivas is an assistant professor of computer science at Baylor University in Texas. He worked in industry for a decade as a software engineer before becoming an academic. He is a senior member of the IEEE, ACM, and SIAM. He was formerly at NASA Goddard Space Flight Center performing research. He is an ally of women in technology, a deep learning evangelist, machine learning ethicist, and a proponent of the democratization of machine learning and artificial intelligence in general. He teaches machine learning and deep learning. Dr. Rivas is a published author and all his papers are related to machine learning, computer vision, and machine learning ethics. Dr. Rivas prefers Vim to Emacs and spaces to tabs.