Book Image

Hands-On Mathematics for Deep Learning

By : Jay Dawani
Book Image

Hands-On Mathematics for Deep Learning

By: Jay Dawani

Overview of this book

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.
Table of Contents (19 chapters)
1
Section 1: Essential Mathematics for Deep Learning
7
Section 2: Essential Neural Networks
13
Section 3: Advanced Deep Learning Concepts Simplified

Overview of attention

When we go about our lives (in the real world), our brains don't observe every detail in our environment at all times; instead, we focus on (or pay greater attention to) information that is relevant to the task at hand. For example, when we are driving, we are able to adjust our focal length to focus on different details, some of which are closer and others are further away, and then act on what we observe. Similarly, when we are conversing with others, we usually don't listen carefully to each and every word; we listen to only part of what is spoken and use it to infer the relationships with some of the words to figure out what the other person is saying. Often, when we are reading/listening to someone, we can use some words to infer what the person is going to say next based on what we have already read/heard.

But why do we need these attention...