You're reading from Python Deep Learning Next generation techniques to revolutionize computer vision, AI, speech and data analysis

Product type Hardcover

Published in Nov 2023

Publisher Packt

ISBN-13 9781837638505

Length 362 pages

Edition 1st Edition

Languages

Python

Tools

Keras

Concepts

Deep Learning

Authors (4):

Valentino Zocca

Gianmario Spacagna

Daniel Slater

Peter Roelants

Preface

1. Part 1:Introduction to Neural Networks

2. Chapter 1: Machine Learning – an Introduction FREE CHAPTER

3. Chapter 2: Neural Networks

4. Chapter 3: Deep Learning Fundamentals

5. Part 2: Deep Neural Networks for Computer Vision

6. Chapter 4: Computer Vision with Convolutional Networks

7. Chapter 5: Advanced Computer Vision Applications

8. Part 3: Natural Language Processing and Transformers

9. Chapter 6: Natural Language Processing and Recurrent Neural Networks

10. Chapter 7: The Attention Mechanism and Transformers

11. Chapter 8: Exploring Large Language Models in Depth

12. Chapter 9: Advanced Applications of Large Language Models

13. Part 4: Developing and Deploying Deep Neural Networks

14. Chapter 10: Machine Learning Operations (MLOps)

15. Index

16. Other Books You May Enjoy

The structure of a convolutional network

The following figure shows the structure of a basic classification CNN:

Figure 4.10 – A basic convolutional network with convolutional, FC, and pooling layers

Most CNNs share basic properties. Here are some of them:

We would typically alternate one or more convolutional layers with one pooling layer (or a stride convolution). In this way, the convolutional layers can detect features at every level of the receptive field size. The aggregated receptive field size of deeper layers is larger than the ones at the beginning of the network. This allows them to capture more complex features from larger input regions. Let’s illustrate this with an example. Imagine that the network uses 3×3 convolutions with stride = 1 and 2×2 pooling with stride = 2:
- The units of the first convolutional layer will receive input from 3×3 pixels of the image.
- A group of 2×2 output units of the first...