The primary objective of the training process is to discover a function that takes into account a pixel’s position (X, Y, and any other activated inputs) and produces a color (a set of 3 RGB numbers) based on the given data points. These data points refer to the “example” points, represented by dots that can be placed using the color selector buttons and left-clicking to add new points. The right panel provides an option to clear these points.
(I hope this helps!)
Sorry if my previous response wasn’t that clear.
The network learns through “gradient descent.”
Gradient descent is a fundamental optimization algorithm used in training neural networks. It works by iteratively adjusting the network’s parameters to minimize the difference between its predicted outputs and the actual targets in the training data.
The process starts with initializing the parameters randomly. Then, for each training example, the network calculates the error between its prediction and the target output. The gradients of the parameters with respect to this error are computed, indicating the direction and magnitude of the steepest descent in the error space.
The parameters are updated by subtracting a fraction of the gradients, known as the learning rate, from their current values. This step is repeated for all training examples in multiple iterations or epochs, gradually reducing the error.
By repeatedly adjusting the parameters based on the gradients, the neural network “descends” the error surface, converging towards a set of parameter values that yield better predictions. This iterative process enables the network to learn and improve its performance over time.
— ChatGPT
Think of the gradients like slope. You are always trying to determine which way is “downhill” and move towards the lowest possible point. A good visualization of gradient descent is this video: https://youtu.be/hfMk-kjRv4c?t=907 (from 15:07).
