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

The basic artificial neuron

The building block of a neural network is an abstraction of a biological neuron, a quite simplistic but powerful computational unit that was proposed for the first time by F. Rosenblatt in 1957 to make up the simplest neural architecture, called a perceptron, which we are going to analyze in the next section. Contrary to Hebbian learning, which is more biologically plausible but has some strong limitations, the artificial neuron was designed with a pragmatic viewpoint and only its structure is based on a few of the elements that characterize a biological neuron.

However, recent deep learning research activities have unveiled the enormous power of this kind of architecture. Even though there are more complex and specialized computational cells, the basic artificial neuron can be summarized as the conjunction of two blocks, which are clearly shown in the following diagram: