Neural networks typically train by using large datasets to adjust the weights of their connections and optimize their ability to learn from new information.

The process starts with an initial set of random weights, which are then updated through a repeated cycle of forward and backward propagation, where the network calculates its output given a particular input, compares it to the desired output, and makes adjustments to the weights accordingly. This process continues until the network reaches a satisfactory level of accuracy in predicting new data points.

Thus, training the neural network involves adjusting the weights and offsets of the neurons in the network to minimize the error between its output and the desired outcome. This is done iteratively through multiple rounds of forward propagation (where inputs are passed through the network and an output is generated) and backpropagation (where errors are propagated backwards through the layers and used to update weights and biases).

Gradient descent comes into play when calculating these updates, by finding the direction in which weights should be adjusted to bring about a slight decrease in error. It may sound daunting but with guidance from experts, one can easily carry out training using gradient descent algorithm.

Once trained, neural networks are tested by running them on a separate set of data that they haven’t seen before and comparing their predicted outputs to the actual values. A well-trained network will be able to accurately predict these values and generalize its knowledge to new data points beyond its training set.

In this post I will show you an example of training a simple neural network in Python using the gradient descent method.