Neural Networks: A Comprehensive Vision

[bitheerani93]

Neural networks are a mathematical model inspired by the human brain, fundamental in the field of artificial intelligence. They consist of artificial neurons organized in layers, and their design allows them to perform various complex tasks.

Fundamental Structure
A typical neural network consists of three main layers:

Input Layer : Receives the input data, where each neuron represents a specific feature.
Hidden Layers : Process information afghanistan phone number list learn complex patterns. These layers are essential in deep learning.
Output Layer : Generates the prediction or result, adapting to the type of problem to be solved.
Varieties of Neural Networks
There are several specialized types of neural networks:

Densely Connected Neural Networks : Each neuron in a layer is connected to all neurons in adjacent layers.
Convolutional Neural Networks (CNN) : Ideal for grid-structured data (such as images), they use convolutional layers to identify local patterns.
Recurrent Neural Networks (RNN) : Designed to handle sequences of data, they maintain information over time through feedback connections.
The Learning Process
Training a neural network is done using backpropagation , which consists of five key stages:

Initialization : Random setting of weights and biases.
Forward Pass : Calculating the prediction with the input data.
Error Calculation : Comparing the prediction with the actual value using a loss function.
Backpropagation : Calculation of error gradients in reverse, applying the chain rule.
Weight Update : Adjusting weights to minimize error, using algorithms such as gradient descent.