Book Image

Machine Learning for Finance

By : Jannes Klaas
Book Image

Machine Learning for Finance

By: Jannes Klaas

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.
Table of Contents (15 chapters)
Machine Learning for Finance
Other Books You May Enjoy

Working with big image datasets

Images tend to be big files. In fact, it's likely that you will not be able to fit your entire image dataset into your machine's RAM.

Therefore, we need to load the images from disk "just in time" rather than loading them all in advance. In this section, we will be setting up an image data generator that loads images on the fly.

We'll be using a dataset of plant seedlings in this case. This was provided by Thomas Giselsson and others, 2017, via their publication, A Public Image Database for Benchmark of Plant Seedling Classification Algorithms.

This dataset is available from the following link:

You may be wondering why we're looking at plants; after all, plant classifications are not a common problem that is faced in the finance sector. The simple answer is that this dataset lends itself to demonstrating many common computer vision techniques and is available under an open domain license; it's therefore a great training dataset...