Video Manual - Neural Networks Explained in Plain English

The goal of the webinar is to demystify neural networks, explain neural networks in plain English, and share easy to understand code examples how NN can be used.

• Artificial Intelligence: History and Background
• Neural Networks: The Basics
• Case Example
• Problems with Neural Networks
• Solutions to common problems with Neural Networks

In quantitative finance, neural networks are most often used for time-series forecasting, proprietary trading signal generation, fully automated trading (decision making), financial modelling, derivatives pricing, credit risk assessments, pattern matching, and classification of securities.

1. Neural networks are not models of the human brain
2. Neural networks are not just a "weak form" of statistics
3. Neural networks come in many different architectures
4. Size matters, but bigger isn't always better
5. Many training algorithms exist for neural networks
6. Neural networks do not always require a lot of data
7. Neural networks cannot be trained on any data
8. Neural networks may need to be retrained
9. Neural networks are not black boxes
10. Neural networks are not hard to implement