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

Parameter tying and sharing

The preceding parameter norm penalties work by penalizing the model parameters when they deviate from 0 (a fixed value). But sometimes, we may want to express prior knowledge about which parameters would be better suited to the model. Although we may not know what those parameters are, thanks to domain knowledge and the architecture of the model, we know that there are likely to be some dependencies between the parameters of the model.

These dependencies could be some specific parameters that are closer to some than to others. Let's suppose we have two different models for a classification task and detect the same number of classes. Their input distributions, however, are not the same. Let's name the first model A with θ(A) parameters and the second model B with θ(B) parameters. Both of these models map their respective inputs...