Hands-On Mathematics for Deep Learning

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

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 learning the math behind it. Working knowledge of the Python programming language and machine learning basics is required.