Book description
With the resurgence of neural networks in the 2010s, deep learning has become essential for machine learning practitioners and even many software engineers. This book provides a comprehensive introduction for data scientists and software engineers with machine learning experience. You’ll start with deep learning basics and move quickly to the details of important advanced architectures, implementing everything from scratch along the way.
Author Seth Weidman shows you how neural networks work using a first principles approach. You’ll learn how to apply multilayer neural networks, convolutional neural networks, and recurrent neural networks from the ground up. With a thorough understanding of how neural networks work mathematically, computationally, and conceptually, you’ll be set up for success on all future deep learning projects.
This book provides:
 Extremely clear and thorough mental models—accompanied by working code examples and mathematical explanations—for understanding neural networks
 Methods for implementing multilayer neural networks from scratch, using an easytounderstand objectoriented framework
 Working implementations and clearcut explanations of convolutional and recurrent neural networks
 Implementation of these neural network concepts using the popular PyTorch framework
Publisher resources
Table of contents
 Preface

1. Foundations
 Functions
 Derivatives
 Nested Functions
 The Chain Rule
 A Slightly Longer Example
 Functions with Multiple Inputs
 Derivatives of Functions with Multiple Inputs
 Functions with Multiple Vector Inputs
 Creating New Features from Existing Features
 Derivatives of Functions with Multiple Vector Inputs
 Vector Functions and Their Derivatives: One Step Further
 Computational Graph with Two 2D Matrix Inputs
 The Fun Part: The Backward Pass
 Conclusion
 2. Fundamentals

3. Deep Learning from Scratch
 Deep Learning Definition: A First Pass
 The Building Blocks of Neural Networks: Operations
 The Building Blocks of Neural Networks: Layers
 Building Blocks on Building Blocks
 The NeuralNetwork Class, and Maybe Others
 Deep Learning from Scratch
 Trainer and Optimizer
 Putting Everything Together
 Conclusion and Next Steps
 4. Extensions
 5. Convolutional Neural Networks
 6. Recurrent Neural Networks
 7. PyTorch
 A. Deep Dives
 Index
Product information
 Title: Deep Learning from Scratch
 Author(s):
 Release date: September 2019
 Publisher(s): O'Reilly Media, Inc.
 ISBN: 9781492041412
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