Hands-On Mathematics for Deep Learning: Build a solid mathematical foundation for training efficient deep neural networks 1st Edition by Jay Dawani

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Hands-On Mathematics for Deep Learning: Build a solid mathematical foundation for training efficient deep neural networks 1st Edition by Jay Dawani PDF

Author: Jay Dawani

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.
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.
Table of Contents

Linear Algebra
Vector Calculus
Probability and Statistics
Optimization
Graph Theory
Linear Neural Networks
Feedforward Neural Networks
Regularization
Convolutional Neural Networks
Recurrent Neural Networks
Attention Mechanisms
Generative Models
Transfer and Meta Learning
Geometric Deep Learning