You're reading from Artificial Intelligence with Python - Second Edition

Product type Book

Published in Jan 2020

Publisher Packt

ISBN-13 9781839219535

Pages 618 pages

Edition 2nd Edition

Languages

Python

Concepts

Artificial Intelligence

Author (1):

Prateek Joshi

Table of Contents (26) Chapters

Preface

1. Introduction to Artificial Intelligence

2. Fundamental Use Cases for Artificial Intelligence

3. Machine Learning Pipelines

4. Feature Selection and Feature Engineering

5. Classification and Regression Using Supervised Learning

6. Predictive Analytics with Ensemble Learning

7. Detecting Patterns with Unsupervised Learning

8. Building Recommender Systems

9. Logic Programming

10. Heuristic Search Techniques

11. Genetic Algorithms and Genetic Programming

12. Artificial Intelligence on the Cloud

13. Building Games with Artificial Intelligence

14. Building a Speech Recognizer

15. Natural Language Processing

16. Chatbots

17. Sequential Data and Time Series Analysis

18. Image Recognition

19. Neural Networks

20. Deep Learning with Convolutional Neural Networks

21. Recurrent Neural Networks and Other Deep Learning Models

22. Creating Intelligent Agents with Reinforcement Learning

23. Artificial Intelligence and Big Data

24. Other Books You May Enjoy

25. Index

Types of layers in a CNN

CNNs typically use the following types of layers:

Input layer – This layer takes the raw image data as it is.

Convolutional layer – This layer computes the convolutions between the neurons and the various patches in the input. If you need a quick refresher on image convolutions, you can check out this link:

http://web.pdx.edu/~jduh/courses/Archive/geog481w07/Students/Ludwig_ImageConvolution.pdf

The convolutional layer basically computes the dot product between the weights and a small patch in the output of the previous layer.

Rectified Linear Unit layer – This layer applies an activation function to the output of the previous layer. This function is usually something like max(0, x). This layer is needed to add non-linearity to the network so that it can generalize well to any type of function.

Pooling layer – This layer samples the output of the previous layer resulting in a structure with smaller dimensions...