Book Image

Machine Learning Quick Reference

By : Rahul Kumar
Book Image

Machine Learning Quick Reference

By: Rahul Kumar

Overview of this book

Machine learning makes it possible to learn about the unknowns and gain hidden insights into your datasets by mastering many tools and techniques. This book guides you to do just that in a very compact manner. After giving a quick overview of what machine learning is all about, Machine Learning Quick Reference jumps right into its core algorithms and demonstrates how they can be applied to real-world scenarios. From model evaluation to optimizing their performance, this book will introduce you to the best practices in machine learning. Furthermore, you will also look at the more advanced aspects such as training neural networks and work with different kinds of data, such as text, time-series, and sequential data. Advanced methods and techniques such as causal inference, deep Gaussian processes, and more are also covered. By the end of this book, you will be able to train fast, accurate machine learning models at your fingertips, which you can easily use as a point of reference.
Table of Contents (18 chapters)
Title Page
Copyright and Credits
About Packt
Contributors
Preface
Index

Compressed sensing


Compressed sensing is one of the easiest problems to solve in the area of information theory and signal processing. It is a signal acquisition and reconstruction technique where the signal is compressible. The signal must be sparse. Compressed sensing tries to fit samples of a signal to functions, and it has a preference to use as few basic functions as possible to match the samples. This is described in the following diagram:

This is one of the prime equations that we see in linear algebra, where y is a M x 1 matrix, phi is a M x N matrix that has got a number of columns that is higher than the number of rows, and x is a N x 1 matrix comprising k non-zero entries. There are so many unknowns, which is expressed as an N length vector and M measurements, wherein M << N. In this type of equation, we know that many solutions are possible since the null space of this matrix is non-trivial. Hence, this equation can accommodate many solutions.

Our goal

Our goal is to find...