Neural Networks - page 28

New comment
 

Simple neural net object 

and the same with matrix vector

for comparison of structure

 

Experiments with neural networks (Part 6): Perceptron as a self-sufficient tool for price forecast - the article 

Experiments with neural networks (Part 6): Perceptron as a self-sufficient tool for price forecast

Perceptron is a machine learning technique that can be used to predict market prices. It is a useful tool for traders and investors striving to get a price forecast.

Experiments with neural networks (Part 6): Perceptron as a self-sufficient tool for price forecast
Experiments with neural networks (Part 6): Perceptron as a self-sufficient tool for price forecast
  • www.mql5.com
The article provides an example of using a perceptron as a self-sufficient price prediction tool by showcasing general concepts and the simplest ready-made Expert Advisor followed by the results of its optimization.
 

Forum on trading, automated trading systems and testing trading strategies

Better NN EA development

Sergey Golubev, 2024.03.08 16:08

The Disagreement Problem: Diving Deeper into The Complexity Explainability in AI

The Disagreement Problem: Diving Deeper into The Complexity Explainability in AI

The disagreement is an open area of research in an interdisciplinary field known as Explainable Artificial Intelligence (XAI). Explainable Artificial Intelligence attempts to help us understand how our models are arriving at their decisions but unfortunately everything is easier said than done. 

We are all aware that machine learning models and available datasets are growing larger and more complex. As a matter of fact, the data scientists who develop machine learning algorithms cannot exactly explain their algorithm’s behaviour across all possible datasets.  Explainable Artificial Intelligence (XAI) helps us build trust in our models, explain their functionality and validate that the models are ready to be deployed in production; but as promising as that may sound, this article will show the reader why we cannot blindly trust any explanation we may get from any application of Explainable Artificial Intelligence technology.


 

Overcoming The Limitation of Machine Learning (Part 1): Lack of Interoperable Metrics

Overcoming The Limitation of Machine Learning (Part 1): Lack of Interoperable Metrics

In our related series of articles, like Self-Optimizing Expert Advisors, we discussed an unsettling truth: even when you follow all the “best practices” in algorithmic trading development, things can still go horribly wrong. Briefly, we observed that practitioners using the RSI according to its standardized rules, may wait several months without the indicator generating any of the expected results. Resulting in trading accounts that are exposed to more market risk than what was intended. 

In this series of articles, we shall explore critical problems that algorithmic traders are exposed to every day, by the very guidelines and practices intended to keep them safe when using machine learning models.
Overcoming The Limitation of Machine Learning (Part 1): Lack of Interoperable Metrics
Overcoming The Limitation of Machine Learning (Part 1): Lack of Interoperable Metrics
  • www.mql5.com
There is a powerful and pervasive force quietly corrupting the collective efforts of our community to build reliable trading strategies that employ AI in any shape or form. This article establishes that part of the problems we face, are rooted in blind adherence to "best practices". By furnishing the reader with simple real-world market-based evidence, we will reason to the reader why we must refrain from such conduct, and rather adopt domain-bound best practices if our community should stand any chance of recovering the latent potential of AI.
1...2122232425262728
New comment