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New York University (NYU)

Deep Learning and Neural Networks for Financial Engineering

New York University (NYU) via edX Professional Education


Deep Learning ventures into territory associated with Artificial Intelligence. This course will demonstrate how neural networks can improve practice in various disciplines, with examples drawn primarily from financial engineering. Students will gain an understanding of deep learning techniques, including how alternate data sources such as images and text can advance practice within finance.


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

  • Recipe for Machine Learning

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

  • Back propagation

  • 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

  • Neural Language Processing (NLP)

  • Interpretation: what is going on inside a Neural Network

  • Attention

  • Adversarial examples

  • Final words

Taught by

Ken Perry


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