Math for Deep Learning: What You Need to Know to Understand Neural Networks
| AUTHOR | Kneusel, Ronald T. |
| PUBLISHER | No Starch Press (12/07/2021) |
| PRODUCT TYPE | Paperback (Paperback) |
Description
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.
Show More
Product Format
Product Details
ISBN-13:
9781718501904
ISBN-10:
1718501900
Binding:
Paperback or Softback (Trade Paperback (Us))
Content Language:
English
More Product Details
Page Count:
344
Carton Quantity:
24
Product Dimensions:
7.00 x 0.90 x 9.10 inches
Weight:
1.40 pound(s)
Feature Codes:
Bibliography,
Index,
Price on Product,
Illustrated
Country of Origin:
CN
Subject Information
BISAC Categories
Computers | Data Science - Machine Learning
Computers | Calculus
Computers | Data Science - Neural Networks
Dewey Decimal:
006.310
Library of Congress Control Number:
2021939724
Descriptions, Reviews, Etc.
publisher marketing
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.
Show More
List Price $49.99
Your Price
$49.49