What Is a Neural Network?
A neural network is a Machine Learning model inspired by the way connected neurons work together. It uses layers of connected nodes to learn patterns from data and make predictions.
The simple idea
A neural network takes input data, processes it through connected layers, and produces an output. During training, it adjusts its internal values so that its predictions become more accurate.
What Is a Neural Network?
A neural network is a model made up of many small processing units called neurons.
These neurons are organized into layers. Each layer receives information, processes it, and passes the result to the next layer.
The network learns how the input information is related to the desired output.
Basic Structure of a Neural Network
A basic neural network contains three important parts: an input layer, one or more hidden layers, and an output layer.
The input layer receives the data. Hidden layers process the information and learn patterns. The output layer produces the final prediction.
Example: Predicting Whether a Student Will Pass
Suppose we want to predict whether a student will pass an exam.
We provide three pieces of information:
These values become the inputs to the neural network.
During training, the neural network looks at many examples of students and learns relationships between their information and their results.
What Is a Neuron?
A neuron is a small processing unit inside a neural network. It receives values, applies weights and a bias, performs a calculation, and produces an output.
The neuron does not simply treat every input as equally important. The weights determine how strongly each input affects the calculation.
Why Do Neurons Have Weights?
Different inputs can have different levels of importance.
For example, when predicting whether a student will pass, previous exam performance might be more useful than another input.
Simple Example
During training, the network adjusts these weights so that its predictions improve.
How Does Information Move Through the Network?
Information moves from one layer to the next. This process is called forward propagation.
Each layer performs calculations and passes its results to the next layer.
How Does a Neural Network Learn?
A neural network does not know the correct weights when it starts training.
It starts with initial values, makes predictions, compares those predictions with the correct answers, and then adjusts its weights.
This process is repeated many times until the model learns useful patterns from the training data.
Example: Recognizing a Cat
A neural network can also process images. An image contains many pixel values that become the input.
The network can learn increasingly complex representations through its layers.
Why Is It Called a Neural Network?
The word "network" comes from the way many neurons are connected together.
The connections allow information to flow through the network and allow the model to learn relationships between inputs and outputs.
Important
Neural networks are inspired by biological neurons, but they are not actual human brains. They are mathematical models implemented using computers.
Check Your Understanding
What is a neural network?
A Machine Learning model made of connected
processing units called neurons.
What are the main parts?
An input layer, hidden layers, and an output layer.
What does a neuron use to calculate its output?
Inputs, weights, a bias, and a mathematical
activation process.
How does the network learn?
It makes predictions, measures errors, and
adjusts its weights during training.