Neural network

A neural network (NN) is a computational model inspired by biological neurons, designed to learn patterns from data. It consists of interconnected layers of artificial neurons (nodes), where each connection has a weight that adjusts during training.

**Key Concepts:

1. Forward Pass: Input data flows through the network, producing predictions, by means of the weights and bias, and an activation function (sigmoid or ReLU).
2. Cost Function (Loss Function): Measures how far predictions are from true targets (e.g., mean squared error).
3. Goal of Training: Minimize the cost function by adjusting weights.
4. The training of a NN: consists of backpropagation+gradient descent.