Deep Learning
A Visual Approach
by Andrew Glassner
Publisher: No Starch Press
Book Details
| Print ISBN | 9781718500723 |
| eText ISBN | 9781718500730 |
| Publisher | No Starch Press |
| Publishing Year | 2021 |
| Language | English |
| Pages | 768 |
Deep Learning: A Visual Approach is a textbook that introduces deep learning concepts through visual and conceptual explanations rather than mathematical equations. Published by No Starch Press, the volume explains how key algorithms operate without requiring advanced technical prerequisites.
The text outlines foundational statistical ideas, probability, and basic machine learning workflows such as classification and data preparation. It also examines neural networks, backpropagation, convolutional models, recurrent networks, and generative adversarial networks.
Designed for learners seeking an in-depth understanding of the field, the book uses full-color illustrations and practical analogies to clarify complex architectures.
Table of Contents
Chapter 1: An Overview of Machine Learning Techniques
Chapter 2: Essential Statistical Ideas
Chapter 3: Probability
Chapter 4: Bayes’ Rule
Chapter 5: Curves and Surfaces
Chapter 6: Information Theory
Chapter 7: Classification
Chapter 8: Training and Testing
Chapter 9: Overfitting and Underfitting
Chapter 10: Data Preparation
Chapter 11: Classifiers
Chapter 12: Ensembles
Chapter 13: Neural Networks
Chapter 14: Backpropagation
Chapter 15: Optimizers
Chapter 16: Convolutional Neural Networks
Chapter 17: Convnets in Practice
Chapter 18: Recurrent Neural Networks
Chapter 19: Autoencoders
Chapter 20: Reinforcement Learning
Chapter 21: Generative Adversarial Networks
Chapter 22: Creative Applications
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