Hands-On Mathematics for Deep Learning: Build a solid mathematical foundation for training efficient deep neural networks

Hands-On Mathematics for Deep Learning: Build a solid mathematical foundation for training efficient deep neural networks Packt 第1张
Hands-On Mathematics for Deep Learning: Build a solid mathematical foundation for training efficient deep neural networks
By 作者:Jay Dawani
pages 页数: 364 pages
Publisher Finelybook 出版社: Packt Publishing (12 Jun. 2020)
Language 语言: English
ISBN-10 书号:1838647295
ISBN-13 书号:9781838647292
The Book Description robot was collected from Amazon and arranged by Finelybook
A comprehensive guide to getting well-versed with the mathematical techniques for building modern deep learning architectures
Most programmers and data scientists struggle with mathematics, having either overlooked or forgotten core mathematical concepts. This book uses Python libraries to help you understand the math required to build deep learning (DL) models.
You’ll begin By 作者:learning about core mathematical and modern computational techniques used to design and implement DL algorithms. This book will cover essential topics, such as linear algebra, eigenvalues and eigenvectors, the singular value decomposition concept, and gradient algorithms, to help you understand how to train deep neural networks. Later chapters focus on important neural networks, such as the linear neural network and multilayer perceptrons, with a primary focus on helping you learn how each model works. As you advance, you will delve into the math used for regularization, multi-layered DL, forward propagation, optimization, and backpropagation techniques to understand what it takes to build full-fledged DL models. Finally, you’ll explore CNN, recurrent neural network (RNN), and GAN models and their application.
By the end of this book, you’ll have built a strong foundation in neural networks and DL mathematical concepts, which will help you to confidently research and build custom models in DL.
What you will learn

Understand the key mathematical concepts for building neural network models
Discover core multivariable calculus concepts
Improve the performance of deep learning models using optimization techniques
Cover optimization algorithms, from basic stochastic gradient descent (SGD) to the advanced Adam optimizer
Understand computational graphs and their importance in DL
Explore the backpropagation algorithm to reduce output error
Cover DL algorithms such as convolutional neural networks (CNNs), sequence models, and generative adversarial networks (GANs)

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