How do neural networks work in machine learning ?

A neural network is a method in artificial intelligence that teaches computers to process data in a way that is inspired by the human brain. It is a type of machine learning process, called deep learning, that uses interconnected nodes or neurons in a layered structure that resembles the human brain.

How do neural networks work step by step?

Input level:
This level receives the initial data or attributes. Each neuron in the input layer represents a feature.

Hidden level:
Between the input and output levels, there may be more than one hidden level. Each hidden level is composed of many neurons. The purpose of these layers is to extract and learn features from the input data.
Weights and Redundancy: Each level has a weight associated with the pairs between two migratory neurons. These weights determine the strength of the relationship and are adjusted to reduce cognitive error during the training process.
Activation Function:
Each neuron applies an activation function with a weighted contribution and redundancy of its migratory input. This activation function introduces non-linearity into the network, allowing it to learn complex patterns in the data.
Output Level:
The output layer produces the final prediction or output of the network. The number of neurons in the output level depends on the type of problem.
Further progress:
During forward progression, initial data is fed into the network, and the activations of each neuron are examined level by level until the output level has produced a prediction.
Loss Function:
A loss function measures how well the network’s prediction fits the actual target.