What is a Neural Network

A neural network is a machine learning model that loosely mimics how the human brain processes information, using layers of connected units called neurons.

Data enters through an input layer, passes through one or more hidden layers where calculations happen, and produces a final result through an output layer; each connection carries a weight that adjusts as the model learns.

Key Components

• Neurons: The basic units that receive input, process it, and pass output forward.
• Weights: Values that determine how much influence one neuron has on another.
• Bias: An extra adjustable value that helps the model fit data more flexibly.
• Activation Function: Decides whether a neuron's output should pass forward.

How It Works

Each neuron takes input values, multiplies them by their respective weights, adds a bias, and passes the result through an activation function.

This output then becomes the input for the next layer, and the process repeats until the final output layer produces a prediction, which is compared against the actual answer to adjust the weights during training.

Basic Neuron Calculation

output = activation(weight * input + bias)

Importance of Neural Networks

• They can learn complex, non linear patterns that simpler models often miss.
• They form the foundation behind image recognition, speech processing, and translation systems.
• They scale well with large amounts of data, often improving as more data becomes available.
• They power modern deep learning systems used across almost every AI application today.