A Practical Introduction to Artificial Neural Networks for Finance

Instructed by:

Wian Stipp

Take a deep dive into deep learning. Learn how to build Deep Learning models from scratch using Keras and Tensorflow 2. You will learn all the essentials needed to deploy and improve DNNs on a variety of Finance related problems, such as Credit Card Default Prediction.

Pricing: Free
Part One: Welcome

Welcome to the course!

 In the first part we will talk about what you will be able to do after completing the course and what the prerequisites are.

Part Two: Introduction to Artificial Neural Networks

We will discuss how biology and neurology inspired the field of deep learning. We then discuss artificial neurons, how they are different from their biological cousins, and how they can be tied together to form a neural network. You will learn the specifics of how the neural network learns through backpropagation and gradient descent. This part of the course is aimed to build your intuition as to how an ANN works, while avoiding the mathematical side.

Part Three: Keras for Deep Learning

Here we start building the models themselves. You will learn exactly how to construct, compile, train, evaluate and make predictions with your neural network. You will see issues that classification tasks have with under-represented classes and how we can fix this with weight adjustments.

Part Four: Improving Deep Neural Networks

This section contains the bulk of the course. We really want you to have a broad knowledge of everything you can do to get your model performing extremely well. This includes:

  • Hyperparameters

  • Hidden layers

  • Number of Neurons

  • Initialisation Logic

  • Activation Functions and Non-Saturating Activation Functions

  • Normalisation methods

  • Momentum, Adaptive and Hybrid Optimisation methods

  • Regularisation and Monte Carlo Dropout

The course is then concluded with a final project. This workbook will get you building a neural network using all the lessons learned in the course, as well as getting you to think. We will use data from 30,000 customers that use credit cards to predict if they will default on their payments next month. We use Monte Carlo dropout to make risk adjusted predictions to give us a better risk and cost profile.​

Downloadable Course Projects

We provide easy downloads for all of our workbooks and data. Our workbooks are gradeded automatically and the answers are provided in a seperate workbook. 

Beginner Level

You should have some experience in coding Python, as well as basic familiarity with scientific libraries such as Numpy and Pandas. There is very little mathematical/statistical background for this particular course since we are just focussing on building and deploying models

Study at your own pace

The course content is all there for whenever you need it. Work whenever and however you desire!


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