Methods & Tools

To support learning from experience

Reinforcement Learning:

• Mechanism of Operation:

The system is "rewarded" for generating accurate results and "penalized" for inaccuracies, thereby adjusting its behavior to improve outcomes.

• Financial Application

If a stock price prediction is correct, the system receives a "reward" by increasing the weight assigned to that predictive model.

Conversely, if the prediction is incorrect, the system reduces the weight and adjusts the model to improve future performance.

Transfer Learning:

• Mechanism of Operation:

-STRANT AI leverages knowledge acquired from solving one problem to enhance performance in similar problems.

• Real Estate Application Example:

If the AI learns how to predict real estate values in an urban area, this acquired knowledge can be transferred and applied to predict real estate values in suburban areas.

Continuous Learning:

• Mechanism of Operation:

The system continuously updates and retrains its models with new data without requiring downtime for complete retraining from scratch.

• Advantages:

The system remains consistently updated with the latest information.

Ensures rapid responsiveness to unforeseen changes