Training a neural network
There are three basic training approaches:
Supervised learning - With supervised learning the model is trained with data that matches input sets to output values
Unsupervised learning - In unsupervised learning, the data does not contain results, but the model is expected to determine relationships on its own
Reinforcement learning - Similar to supervised learning, but a reward is provided for good results
These datasets differ in the information they contain. Supervised and reinforcement learning contain correct output for a set of input. The unsupervised learning does not contain correct results.
A neural network learns (at least with supervised learning) by feeding an input into a network and comparing the results, using the activation function, to the expected outcome. If they match, then the network has been trained correctly. If they don't match then the network is modified.
When we modify the weights we need to be careful not to change them too drastically....
