Book Image

Designing Machine Learning Systems with Python

By : David Julian
Book Image

Designing Machine Learning Systems with Python

By: David Julian

Overview of this book

Machine learning is one of the fastest growing trends in modern computing. It has applications in a wide range of fields, including economics, the natural sciences, web development, and business modeling. In order to harness the power of these systems, it is essential that the practitioner develops a solid understanding of the underlying design principles. There are many reasons why machine learning models may not give accurate results. By looking at these systems from a design perspective, we gain a deeper understanding of the underlying algorithms and the optimisational methods that are available. This book will give you a solid foundation in the machine learning design process, and enable you to build customised machine learning models to solve unique problems. You may already know about, or have worked with, some of the off-the-shelf machine learning models for solving common problems such as spam detection or movie classification, but to begin solving more complex problems, it is important to adapt these models to your own specific needs. This book will give you this understanding and more.
Table of Contents (16 chapters)
Designing Machine Learning Systems with Python
Credits
About the Author
About the Reviewer
www.PacktPub.com
Preface
Free Chapter
1
Thinking in Machine Learning
Index

Chapter 6. Neural Networks

Artificial neural networks, as the name suggests, are based algorithms that attempt to mimic the way neurons work in the brain. Conceptual work began in the 1940s, but it is only somewhat recently that a number of important insights, together with the availability of hardware to run these more computationally expensive models, have given neural networks practical application. They are now state-of-the-art techniques that are at the heart of many advanced machine learning applications.

In this chapter, we will introduce the following topics:

  • Logistic units

  • The cost function for neural networks

  • Implementing a neural network

  • Other neural network architectures