![Video Manual - Neural Networks Explained in Plain English Video Manual - Neural Networks Explained in Plain English](https://c.mql5.com/6/521/splash-513228.png)
![Sergey Golubev Sergey Golubev](https://c.mql5.com/avatar/2012/12/50D09349-3D90.jpg)
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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.
- Neural networks are not models of the human brain
- Neural networks are not just a "weak form" of statistics
- Neural networks come in many different architectures
- Size matters, but bigger isn't always better
- Many training algorithms exist for neural networks
- Neural networks do not always require a lot of data
- Neural networks cannot be trained on any data
- Neural networks may need to be retrained
- Neural networks are not black boxes
- Neural networks are not hard to implement