Hands-On Mathematics for Deep Learning
The main aim of this book is to make the advanced mathematical background accessible to someone with a programming background. This book will equip the readers with not only deep learning architectures but the mathematics behind them. With this book, you will understand the relevant mathematics that goes behind building deep learning models.
Offered by
Difficulty Level
Intermediate
Completion Time
12h8m
Language
English
About Book
Who Is This Book 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.
Hands-On Mathematics for Deep Learning
- About Book
- Who Is This Book For?
- Book Content
Book content
chapters • 12h8m total length
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
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