- From www.edx.org
Deep Learning and Neural Networks for Financial Engineering
- Self-paced
- Course from 799 €
- 7 Sequences
- Intermediate Level
Course details
Syllabus
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
Prerequisite
The course is intended for financial professionals (analysts, portfolio managers, traders, quants, advisers) and other practitioners with an interest in finance. Solid programming skills are advised; knowledge of Python is an advantage. Students should also have knowledge of basic probability, statistical techniques (including linear regression), calculus; linear algebra. A background (perhaps through the first course of this series) in Classical Machine Learning is helpful but not mandatory.
Instructors
Ken Perry
Adjunct Professor
New York University Tandon School of Engineering
Platform
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