How does a Neural Network "remember" what its learned?

Question

Im trying to wrap my head around understanding neural networks and from everything I've seen, I understand that they are made up of layers created by nodes. These nodes are attached to each other with "weighted" connections, and by passing values through the input layer, the values travel through the nodes, changing their values dependent on the "weight" of the connections (right?). Eventually they reach the output layer with a value. I understand the process but I don't see how this leads to the network being trained. Does the network remember a pattern between weighted connections? How does it remember that pattern?

Answer 1

Each weight and bias on each node is like a stored variable. As new data causes its weights and biases to change, these variables change. Eventually a trained algorithm is done and the weights and biases don't need to change anymore. You can then store the information about the all the nodes, weights, biases and connections however you like. This information is your model. So the "remembering" is just the values of the weights and biases.

Answer 2

Neural network remembers what its learned through its weights and biases. Lets explain it with a binary classification example. During forward propagation, the value computed is the probability(say p) and actual value is y. Now, loss is calculated using the formula:-> -(ylog(p) + (1-y)log(1-p)). Once the loss is calculated, this info is propagated backwards and corresponding derivatives of weights and biases are calculated using this loss. Now weights and biases are adjusted according to these derivatives. In one epoch, all the examples present are propagated and weights and biases are adjusted. Then, same examples are propagated forward and backward and correspondingly in each step, weights and biases are adjusted. Finally, after minimizing the loss to a good extent or, achieving a high accuracy (make sure not to overfit), we can store the value of weights and biases and this is what neural network has learned.