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  • Book Overview & Buying Hands-On Mathematics for Deep Learning
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Hands-On Mathematics for Deep Learning

Hands-On Mathematics for Deep Learning

By : Dawani
3.5 (10)
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Hands-On Mathematics for Deep Learning

Hands-On Mathematics for Deep Learning

3.5 (10)
By: 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)
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1
Section 1: Essential Mathematics for Deep Learning
7
Section 2: Essential Neural Networks
13
Section 3: Advanced Deep Learning Concepts Simplified

Vector Calculus

Most of you will likely have had some exposure to calculus in the past, be it in high school, college, or university, and were likely hoping to never have to deal with it again. However, calculus is not only one of the most profound discoveries in mathematics; it also plays a vital role in deep learning.

In this chapter, we will start by introducing core concepts of calculus using single variable calculus, and then we will move on to multivariable calculus and extend everything we learned in multivariable calculus to gain an understanding of vector calculus and its relation to deep learning.

This chapter will cover the following topics:

  • Single variable calculus
  • Multivariable calculus
  • Vector calculus
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Hands-On Mathematics for Deep Learning
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