You're reading from Mastering Machine Learning Algorithms Expert techniques for implementing popular machine learning algorithms, fine-tuning your models, and understanding how they work

Product type Paperback

Published in Jan 2020

Publisher Packt

ISBN-13 9781838820299

Length 798 pages

Edition 2nd Edition

Languages

Python

Tools

Keras

Concepts

Machine Learning

Authors (2):

Giuseppe Bonaccorso

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Table of Contents (28) Chapters

Preface

1. Machine Learning Model Fundamentals

2. Loss Functions and Regularization FREE CHAPTER

3. Introduction to Semi-Supervised Learning

4. Advanced Semi-Supervised Classification

5. Graph-Based Semi-Supervised Learning

6. Clustering and Unsupervised Models

7. Advanced Clustering and Unsupervised Models

8. Clustering and Unsupervised Models for Marketing

9. Generalized Linear Models and Regression

10. Introduction to Time-Series Analysis

11. Bayesian Networks and Hidden Markov Models

12. The EM Algorithm

13. Component Analysis and Dimensionality Reduction

14. Hebbian Learning

15. Fundamentals of Ensemble Learning

16. Advanced Boosting Algorithms

17. Modeling Neural Networks

18. Optimizing Neural Networks

19. Deep Convolutional Networks

20. Recurrent Neural Networks

21. Autoencoders

22. Introduction to Generative Adversarial Networks

23. Deep Belief Networks

24. Introduction to Reinforcement Learning

25. Advanced Policy Estimation Algorithms

26. Other Books You May Enjoy

27. Index

Summary

In this chapter, we have presented the concept of a deep convolutional network, which is a generic architecture that can be employed in any visual processing task. The idea is based on hierarchical information management, aimed at extracting features starting from low-level elements and moving forward to the high-level details that can be helpful to achieve specific goals.

We discussed the concept of convolution and how it's applied in discrete and finite samples. We followed on by defining the properties of standard convolution, before analyzing some important variants such as atrous (or dilated) convolution, separable (and depth wise separable) convolution, and, eventually, transpose convolution. All these methods can work with 1D, 2D, and 3D samples, even if the most diffused applications are based on bidimensional (not considering the channels) matrices representing static images. In the same section, we also discussed how pooling layers can be employed to reduce...