Math for Deep Learning

“An excellent resource for anyone looking to gain a solid foundation in the mathematics underlying deep learning algorithms. The book is accessible, well-organized, and provides clear explanations and practical examples of key mathematical concepts. I highly recommend it to anyone interested in this field.”—Daniel Gutierrez, insideBIGDATA“Ronald T. Kneusel has written a handy and compact guide to the mathematics of deep learning. It will be a well-worn reference for equations and algorithms for the student, scientist, and practitioner of neural networks and machine learning. Complete with equations, figures and even sample code in Python, this book is a wonderful mathematical introduction for the reader.”—David S. Mazel, Senior Engineer, Regulus-Group“What makes Math for Deep Learning a stand-out, is that it focuses on providing a sufficient mathematical foundation for deep learning, rather than attempting to cover all of deep learning, and introduce the needed math along the way. Those eager to master deep learning are sure to benefit from this foundation-before-house approach.”—Ed Scott, Ph.D., Solutions Architect & IT Enthusiast

Ronald T. Kneusel earned a PhD in machine learning from the University of Colorado, Boulder. He has over 20 years of machine learning industry experience. Kneusel is also the author of Numbers and Computers (2nd ed., Springer 2017), Random Numbers and Computers (Springer 2018), and Practical Deep Learning- A Python-Based Introduction (No Starch Press 2021).