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
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The building blocks of ConvNets in Keras

In this section, we will be building a simple ConvNet that can be used for classifying the MNIST characters, while at the same time, learning about the different pieces that make up modern ConvNets.

We can directly import the MNIST dataset from Keras by running the following code:

from keras.datasets import mnist
(x_train, y_train), (x_test, y_test) = mnist.load_data()

Our dataset contains 60,000 28x28-pixel images. MNIST characters are black and white, so the data shape usually does not include channels:

out: (60000, 28, 28)

We will take a closer look at color channels later, but for now, let's expand our data dimensions to show that we only have a one-color channel. We can achieve this by running the following:

import numpy as np
x_train = np.expand_dims(x_train,-1)
x_test = np.expand_dims(x_test,-1)
out: (60000, 28, 28, 1)

With the code being run, you can see that we now have a single color channel added.


Now we come...