Math for Deep Learning
What You Need to Know to Understand Neural Networks
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ISBN-13:
9781718501904
Veröffentl:
2021
Erscheinungsdatum:
07.12.2021
Seiten:
316
Autor:
Ronald T. Kneusel
Gewicht:
636 g
Format:
232x177x24 mm
Sprache:
Englisch
Beschreibung:
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.
IntroductionChapter 1: Setting the StageChapter 2: ProbabilityChapter 3: More ProbabilityChapter 4: StatisticsChapter 5: Linear AlgebraChapter 6: More Linear AlgebraChapter 7: Differential CalculusChapter 8: Matrix CalculusChapter 9: Data Flow in Neural NetworksChapter 10: BackpropagationChapter 11: Gradient DescentAppendix: Going Further
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