Deep Learning and Neural Networks for Financial Engineering provided by edX is a comprehensive online course, which lasts for 7 weeks long, 4-6 hours a week. Deep Learning and Neural Networks for Financial Engineering is taught by Ken Perry. Upon completion of the course, you can receive an e-certificate from edX. The course is taught in Englishand is Paid Course. Visit the course page at edX for detailed price information.
7 weeks long, 4-6 hours a week
21st, Oct 2022
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
