Ronald T. Kneusel

Math for Deep Learning

What You Need to Know to Understand Neural Networks

• Broschiertes Buch

Produktbeschreibung

Math for Deep Learning provides the essential math you need to understand deep learning discussions, explore more complex implementations, and better use the deep learning toolkits.

With Math for Deep Learning, you'll learn the essential mathematics used by and as a background for deep learning.

You ll work through Python examples to learn key deep learning related topics in probability, statistics, linear algebra, differential calculus, and matrix calculus as well as how to implement data flow in a neural network, backpropagation, and gradient descent. You ll also use Python to work through the mathematics that underlies those algorithms and even build a fully-functional neural network.

In addition you ll find coverage of gradient descent including variations commonly used by the deep learning community: SGD, Adam, RMSprop, and Adagrad/Adadelta.

Produktdetails

• Verlag: No Starch Press
• Seitenzahl: 344
• Erscheinungstermin: 7. Dezember 2021
• Englisch
• Abmessung: 232mm x 177mm x 24mm
• Gewicht: 635g
• ISBN-13: 9781718501904
• ISBN-10: 1718501900
• Artikelnr.: 62146675

Autorenporträt

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).

Inhaltsangabe

Introduction
Chapter 1: Setting the Stage
Chapter 2: Probability
Chapter 3: More Probability
Chapter 4: Statistics
Chapter 5: Linear Algebra
Chapter 6: More Linear Algebra
Chapter 7: Differential Calculus
Chapter 8: Matrix Calculus
Chapter 9: Data Flow in Neural Networks
Chapter 10: Backpropagation
Chapter 11: Gradient Descent
Appendix: Going Further

Rezensionen

"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