Hands-on mathematics for deep learning : build a solid mathematical foundation for training efficient deep neural networks / Jay Dawani.

Format:

Book

Author/Creator:

Dawani, Jay, author.

Language:

English

Subjects (All):

Machine learning--Mathematics.

Machine learning.

Physical Description:

1 online resource (364 pages)

Edition:

1st edition

Place of Publication:

Packt Publishing 2020

System Details:

text file

Summary:

A comprehensive guide to getting well-versed with the mathematical techniques for building modern deep learning architectures Key Features Understand linear algebra, calculus, gradient algorithms, and other concepts essential for training deep neural networks Learn the mathematical concepts needed to understand how deep learning models function Use deep learning for solving problems related to vision, image, text, and sequence applications Book Description 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 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) Who this book is for This book is for data scientists, machine learning developers, aspiring deep learning developers, or anyone who wants to understand the foundation of deep learning by l...

Contents:

About Packt Contributors Section 1: Essential Mathematics for Deep Learning Chapter 01: Linear Algebra Chapter 02: Vector Calculus Chapter 03: Probability and Statistics Chapter 04: Optimization Chapter 05: Graph Theory Section 2: Essential Neural Networks Chapter 06: Linear Neural Networks Chapter 07: Feedforward Neural Networks Chapter 08: Regularization Chapter 09: Convolutional Neural Networks Chapter 10: Recurrent Neural Networks Section 3: Advanced Deep Learning Concepts Simplified Chapter 11: Attention Mechanisms Chapter 12: Generative Models Chapter 13: Transfer and Meta Learning Chapter 14: Geometric Deep Learning Other Books You May Enjoy

Notes:

Includes bibliographical references and index.

Online resource; Title from title page (viewed June 12, 2020)

Description based on print version record.

OCLC:

1159163990