This book is for data scientists, machine learning developers, aspiring DL developers, and anyone who wants to gain a deeper understanding of how DL algorithms work by learning the mathematical concepts that form their foundation. Having knowledge of the basics of machine learning is required to gain a better understanding of the topics covered in the book. Having a strong mathematical background is not essential but would be quite helpful in grasping some of the concepts.
Hands-On Mathematics for Deep Learning
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Hands-On Mathematics for Deep Learning
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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.
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Hands-On Mathematics for Deep Learning
Table of Contents (19 chapters)
Preface
Section 1: Essential Mathematics for Deep Learning
Free Chapter
Linear Algebra
Vector Calculus
Probability and Statistics
Optimization
Graph Theory
Section 2: Essential Neural Networks
Linear Neural Networks
Feedforward Neural Networks
Regularization
Convolutional Neural Networks
Recurrent Neural Networks
Section 3: Advanced Deep Learning Concepts Simplified
Attention Mechanisms
Generative Models
Transfer and Meta Learning
Geometric Deep Learning
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