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

Signals


A form of data that is often encountered in primary scientific research is various binary streams. There are specific codecs for video and audio transmission and storage, and often, we are looking for higher-level tools to deal with each specific format. There are various signal sources we might be considering such as from a radio telescopes, sensor on a camera, or the electrical impulses from a microphone. Signals all share the same underlying principles based on wave mechanics and harmonic motion.

Signals are generally studied using time frequency analysis. The central concept here is that a continuous signal in time and space can be decomposed into frequency components. We use what is known as a Fourier Transform to move between the time and frequency domains. This utilizes the interesting fact that states that any given function, including non periodic functions, can be represented by a series of sine and cosine functions. This is illustrated by the following:

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