NN learning using intuition

A Neural Network (NN) is a machine learning algorithm that mimics the behavior of neurons in the human brain. It has a structure similar to our neurons, where each neuron is equipped with a number of weighted inputs that receive signals from other neurons. The signals then pass through an activation function where it is either stimulated or inhibited, merging together to form a connection that is then used to predict an output. When using intuition to train a NN, the goal is to determine which weights will provide the best result. To do this, one observes correlations between inputs and outputs and modifies the weights accordingly. This involves trial and error and can take a long time to get right, but the advantage is that it provides a more natural and organic way to train the model. Additionally, with intuition, any changes to the input data are easily understood, allowing for quick adjustments.