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

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Table of Contents (18) Chapters

Preface

1. Giving Computers the Ability to Learn from Data

2. Training Simple Machine Learning Algorithms for Classification FREE CHAPTER

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

Putting everything together to build a CNN

So far, we've learned about the basic building blocks of convolutional neural networks. The concepts illustrated in this chapter are not really more difficult than traditional multilayer neural networks. Intuitively, we can say that the most important operation in a traditional neural network is the matrix-vector multiplication.

For instance, we use matrix-vector multiplications to pre-activations (or net input) as in Putting everything together to build a CNN . Here, x is a column vector representing pixels, and W is the weight matrix connecting the pixel inputs to each hidden unit. In a convolutional neural network, this operation is replaced by a convolution operation, as in , where X is a matrix representing the pixels in a height x width arrangement. In both cases, the pre-activations are passed to an activation function to obtain the activation of a hidden unit Putting everything together to build a CNN , where is the activation function. Furthermore, recall that subsampling is another building block of a convolutional...