Deep Learning and Neural Networks for Financial Engineering by New York University – edX

This deep learning course delves into AI and shows how neural networks may be applied to a variety of fields, with a concentration on financial applications. It can be found on the edX platform. To recognize various features, the students will study various deep learning algorithms, data sources, photos, and finance literature. Learners will be able to use neural networks and deep learning techniques to develop prediction models in finance by the end of this course.

Furthermore, the students will understand how to use data from multiple sources and apply techniques such as image recognition and natural language processing (NLP) to make predictions. Finally, the students will gain advanced programming abilities to create neural network models for complicated challenges such as portfolio management and optimization, risk management, and expediting other AI-related financial activities.

The course modules are:

Week 0: Classical Machine Learning: Overview

• Guided entry for students who have not taken the first course in the series
• Notational conventions
• Basic ideas: linear regression, classification

Week 1: Introduction to Neural Networks and Deep Learning

• Neural Networks Overview
• Coding Neural Networks: Tensorflow, Keras
• Practical Colab

Week 2: Convolutional Neural Networks

• A neural network is a Universal Function Approximator
• Convolutional Neural Networks (CNN): Introduction
• CNN: Multiple input/output features
• CNN: Space and time

Week 3: Recurrent Neural Networks

• Recurrent Neural Networks (RNN): Introduction
• RNN Overview
• Generating text with an RNN

Week 4: Training Neural Networks

• Backpropagation
• Vanishing and exploding gradients
• Initializing and maintaining weights
• Improving trainability
• How big should my Neural Network be?

Week 5: Interpretation and Transfer Learning

• Interpretation: Preview
• Transfer Learning
• Tensors, Matrix Gradients

Week 6: Advanced Recurrent Architectures

• Gradients of an RNN
• RNN Gradients that vanish and explode
• Residual connections
• Neural Programming
• LSTM
• Attention: introduction

Week 7: Advanced topics

• Natural Language Processing (NLP)
• Interpretation: what is going on inside a Neural Network
• Attention
• Adversarial examples

Instructor: Ken Perry

Level: Intermediate

Duration: 7 weeks

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Price: Pricing details available on Sign-Up

Website: edx.org/course/deep-learning-and-neural-networks-for-financial-engineering