Open Daily: 10am - 10pm | Alley door pickup 10am - 7pm 3038 Hennepin Ave Minneapolis, MN
612-822-4611

  • Open Daily: 10am - 10pm
    Alley-side Pickup: 10am - 7pm

    3038 Hennepin Ave Minneapolis, MN
    612-822-4611

Open Daily: 10am - 10pm | Alley-side Pickup: 10am - 7pm
3038 Hennepin Ave Minneapolis, MN
612-822-4611
Math for Deep Learning: What You Need to Know to Understand Neural Networks

Math for Deep Learning: What You Need to Know to Understand Neural Networks

Kneusel, Ronald T.

Paperback

CalculusGeneral Computers

Currently unavailable to order

ISBN10: 1718501900
ISBN13: 9781718501904
Publisher: No Starch Pr
Published: Dec 7 2021
Pages: 344
Weight: 1.40
Height: 0.90 Width: 7.00 Depth: 9.10
Language: English
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.

Also in

General Computers

View All