Math for Deep Learning: What You Need to Know to Understand Neural Networks
Ronald T. Kneusel
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. Binding Type: PaperbackAuthor: Ronald T. KneuselPublisher: No Starch PressPublished: 12/07/2021ISBN: 9781718501904Pages: 344Weight: 1.40lbsSize: 9.10h x 7.00w x 0.90d
Book Title
Math for Deep Learning: What You Need to Know to Understand Neural Networks
ISBN
9781718501904
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.
Binding Type: Paperback
Author: Ronald T. Kneusel
Publisher: No Starch Press
Published: 12/07/2021
ISBN: 9781718501904
Pages: 344
Weight: 1.40lbs
Size: 9.10h x 7.00w x 0.90d
Binding Type: Paperback
Author: Ronald T. Kneusel
Publisher: No Starch Press
Published: 12/07/2021
ISBN: 9781718501904
Pages: 344
Weight: 1.40lbs
Size: 9.10h x 7.00w x 0.90d
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.
Binding Type: Paperback
Author: Ronald T. Kneusel
Publisher: No Starch Press
Published: 12/07/2021
ISBN: 9781718501904
Pages: 344
Weight: 1.40lbs
Size: 9.10h x 7.00w x 0.90d
Binding Type: Paperback
Author: Ronald T. Kneusel
Publisher: No Starch Press
Published: 12/07/2021
ISBN: 9781718501904
Pages: 344
Weight: 1.40lbs
Size: 9.10h x 7.00w x 0.90d