The Shallow and the Deep : A biased introduction to neural networks and old school machine learning / Michael Biehl.

Format:

Book

Author/Creator:

Biehl, Michael, author.

Contributor:

Open Textbook Library, distributor.

Series:

Open textbook library.

Language:

English

Subjects (All):

Computer science--Textbooks.

Computer science.

Artificial intelligence--Textbooks.

Artificial intelligence.

Physical Description:

1 online resource

Distribution:

Minneapolis, MN : Open Textbook Library

Place of Publication:

Groningen, Netherlands : University of Groningen Press, 2023.

Language Note:

In English.

Summary:

The Shallow and the Deep is a collection of lecture notes that offers an accessible introduction to neural networks and machine learning in general. However, it was clear from the beginning that these notes would not be able to cover this rapidly changing and growing field in its entirety. The focus lies on classical machine learning techniques, with a bias towards classification and regression. Other learning paradigms and many recent developments in, for instance, Deep Learning are not addressed or only briefly touched upon. Biehl argues that having a solid knowledge of the foundations of the field is essential, especially for anyone who wants to explore the world of machine learning with an ambition that goes beyond the application of some software package to some data set. Therefore, The Shallow and the Deep places emphasis on fundamental concepts and theoretical background. This also involves delving into the history and pre-history of neural networks, where the foundations for most of the recent developments were laid. These notes aim to demystify machine learning and neural networks without losing the appreciation for their impressive power and versatility.

Contents:

Preface

From neurons to networks

Learning from example data

The Perceptron

Beyond linear separability

Feed-forward networks for regression and classification

Distance-based classifiers

Model evaluation and regularization

Preprocessing and unsupervised learning

Concluding quote

Appendix A: Optimization

List of figures

List of algorithms

Abbrev. and acronyms

Bibliography

Notes:

Description based on online resource

ISBN:

9789403430270

Access Restriction:

Open Access Unrestricted online access