You're reading from Python Machine Learning, Second Edition Machine Learning and Deep Learning with Python, scikit-learn, and TensorFlow

Product type Paperback

Published in Sep 2017

Publisher Packt

ISBN-13 9781787125933

Length 622 pages

Edition 2nd Edition

Languages

Python

Tools

Scikit-learn

Concepts

Deep Learning

Authors (2):

Vahid Mirjalili

Sebastian Raschka

View More author details

Table of Contents (18) Chapters

Preface

1. Giving Computers the Ability to Learn from Data FREE CHAPTER

2. Training Simple Machine Learning Algorithms for Classification

3. A Tour of Machine Learning Classifiers Using scikit-learn

4. Building Good Training Sets – Data Preprocessing

5. Compressing Data via Dimensionality Reduction

6. Learning Best Practices for Model Evaluation and Hyperparameter Tuning

7. Combining Different Models for Ensemble Learning

8. Applying Machine Learning to Sentiment Analysis

9. Embedding a Machine Learning Model into a Web Application

10. Predicting Continuous Target Variables with Regression Analysis

11. Working with Unlabeled Data – Clustering Analysis

12. Implementing a Multilayer Artificial Neural Network from Scratch

13. Parallelizing Neural Network Training with TensorFlow

14. Going Deeper – The Mechanics of TensorFlow

15. Classifying Images with Deep Convolutional Neural Networks

16. Modeling Sequential Data Using Recurrent Neural Networks

Index

Summary

In this chapter, we learned about CNNs, or convolutional neural networks, and explored the building blocks that form different CNN architectures. We started by defining the convolution operation, then we learned about its fundamentals by discussing 1D as well as 2D implementations.

We also covered subsampling by discussing two forms of pooling operations: max-pooling and average-pooling. Then, putting all these blocks together, we built a deep convolutional neural network and implemented it using the TensorFlow core API as well as the TensorFlow Layers API to apply CNNs for image classification.

In the next chapter, we'll move on to Recurrent Neural Networks (RNN). RNNs are used for learning the structure of sequence data, and they have some fascinating applications, including language translation and image captioning!