#### Overview of this book

Machine Learning for Finance explores new advances in machine learning and shows how they can be applied across the financial sector, including insurance, transactions, and lending. This book explains the concepts and algorithms behind the main machine learning techniques and provides example Python code for implementing the models yourself. The book is based on Jannes Klaas’ experience of running machine learning training courses for financial professionals. Rather than providing ready-made financial algorithms, the book focuses on advanced machine learning concepts and ideas that can be applied in a wide variety of ways. The book systematically explains how machine learning works on structured data, text, images, and time series. You'll cover generative adversarial learning, reinforcement learning, debugging, and launching machine learning products. Later chapters will discuss how to fight bias in machine learning. The book ends with an exploration of Bayesian inference and probabilistic programming.
Machine Learning for Finance
Contributors
Preface
Other Books You May Enjoy
Free Chapter
Applying Machine Learning to Structured Data
Utilizing Computer Vision
Understanding Time Series
Parsing Textual Data with Natural Language Processing
Using Generative Models
Reinforcement Learning for Financial Markets
Privacy, Debugging, and Launching Your Products
Fighting Bias
Bayesian Inference and Probabilistic Programming
Index

## Filters on color images

Of course, our filter technique is not only limited to black-and-white images. In this section we're going to have a look at color images.

The majority of color images consist of three layers or channels, and this is commonly referred to as RGB, the initialism for the three layers. They are made up of one red channel, one blue channel, and one green channel. When these three channels are laid on top of each other, they add up to create the traditional color image that we know.

Taking that concept, an image is therefore not flat, but actually a cube, a three-dimensional matrix. Combining this idea with our objective, we want to apply a filter to the image, and apply it to all three channels at once. We will, therefore, perform an element-wise multiplication between two three-dimensional cubes.

Our 3x3 filter now has a depth of three and thus nine parameters, plus the bias:

An example of a filter cube or convolutional kernel

This cube, which is referred to as a convolutional...