You're reading from Python Machine Learning by Example Build intelligent systems using Python, TensorFlow 2, PyTorch, and scikit-learn

Product type Paperback

Published in Oct 2020

Publisher Packt

ISBN-13 9781800209718

Length 526 pages

Edition 3rd Edition

Languages

Python

Tools

PyTorch

Concepts

Machine Learning

Author (1):

Yuxi (Hayden) Liu

View More author details

Table of Contents (17) Chapters

Preface

1. Getting Started with Machine Learning and Python

2. Building a Movie Recommendation Engine with Naïve Bayes FREE CHAPTER

3. Recognizing Faces with Support Vector Machine

4. Predicting Online Ad Click-Through with Tree-Based Algorithms

5. Predicting Online Ad Click-Through with Logistic Regression

6. Scaling Up Prediction to Terabyte Click Logs

7. Predicting Stock Prices with Regression Algorithms

8. Predicting Stock Prices with Artificial Neural Networks

9. Mining the 20 Newsgroups Dataset with Text Analysis Techniques

10. Discovering Underlying Topics in the Newsgroups Dataset with Clustering and Topic Modeling

11. Machine Learning Best Practices

12. Categorizing Images of Clothing with Convolutional Neural Networks

13. Making Predictions with Sequences Using Recurrent Neural Networks

14. Making Decisions in Complex Environments with Reinforcement Learning

15. Other Books You May Enjoy

16. Index

Getting started with CNN building blocks

Although regular hidden layers (the fully connected layers we have seen so far) do a good job of extracting features from data at certain levels, these representations might be not useful in differentiating images of different classes. CNNs can be used to extract richer and more distinguishable representations that, for example, make a car a car, a plane a plane, or the handwritten letters "y" a "y", "z" a "z", and so on. CNNs are a type of neural network that is biologically inspired by the human visual cortex. To demystify CNNs, I will start by introducing the components of a typical CNN, including the convolutional layer, the nonlinear layer, and the pooling layer.

The convolutional layer

The convolutional layer is the first layer in a CNN, or the first few layers in a CNN if it has multiple convolutional layers. It takes in input images or matrices and simulates the way neuronal cells respond...