Applies neural networks directly to objects, fundamentally transforming programming and machine learning.
Overview
This technique automatically applies neural networks to designated Python classes and their specific attributes. It's designed to facilitate complex operations and learning processes, akin to reinforcement learning, by dynamically creating and training neural networks based on the interactions with these classes.
How It Works
Class and Attribute Identification: The first step is to identify the Python classes and the specific attributes within these classes that you want the neural networks to focus on. Operation with Classes: When operations are performed on these identified classes and attributes, the system automatically initiates the development of neural networks. Setting Objectives and Action Degrees: Users define the objectives and the scope of actions available for the neural networks. This setting guides the learning process and the direction of network adaptation. Neural Network Development: As the system operates with the specified classes and attributes, it develops and refines neural networks, enhancing its performance and decision-making capabilities over time.