What is an artificial neural network? If you only read the formal definition, it can feel abstract very quickly. But once you break it down into inputs, hidden transformations, and outputs, the idea becomes much easier to follow.

This article explains neural networks from three angles: the core concept, an intuitive example, and the kinds of problems neural networks are commonly used to solve.

1. Concept

Artificial Neural Network (ANN), also called Neural Network (NN) or neural-like network, is a mathematical model that mimics the structure and function of biological neural networks. It consists of a large number of neurons connected for computation. In most cases, artificial neural networks can change their internal structure based on external information, making them adaptive systems. Simply put, they have learning capabilities. Modern neural networks are nonlinear statistical data modeling tools.

Neural Network Structure

1. Input Layer: Many neurons (Neurons) receive numerous nonlinear input signals. The input signals are called input vectors and represent the raw input data.

2. Hidden Layer: The main structure. Different neural networks mainly differ in how the hidden layer parameters are adjusted. Each neuron in this layer transforms the given input data into outputs.

3. Output Layer: Outputs the results.

2. Intuitive Example

In the what-is-a-neural-network Stanford University Neural Network open course, there is a simple example about predicting house value.

As shown in the figure, the input consists of four parameters about the house: house size, number of bedrooms, postal code, and wealth level. The first layer neurons receive the same parameters, but their outputs are different. For example, the top neuron maps to the number of family members, so its relationship with postal code and wealth level is relatively smaller compared to size and number of bedrooms. Accordingly, weights for X1 and X2 are larger, while weights for X3 and X4 are smaller. Thus, the outputs of the first layer neurons are all different.

The outputs from the first layer serve as inputs to the second layer. The second layer neurons have three input parameters and produce output Y through a transformation function. During this process, the detailed weights and biases for each input are automatically adapted through learning. What we need to do is set the number of network layers, number of neurons, and transformation functions, and then provide enough data. The neural network will automatically learn and predict the house price based on the given conditions.

How to Read a Neural Network Diagram Quickly

When beginners first see a neural network diagram, it is easy to get lost in the circles and lines. A simpler reading order is:

• The left side is the input layer, which represents the original features.
• The middle part is one or more hidden layers, where the model transforms and combines those features.
• The right side is the output layer, which represents the final prediction.
• The lines can be understood as influence paths, and training is largely the process of adjusting the weights attached to those paths.

Once these four pieces are clear, later topics such as activation functions, backpropagation, and loss functions become much easier to place in context.

3. Applications

Neural networks are basically used in supervised learning. The table below describes related application examples in detail.

Different Visual Representations of Neural Networks

Summary

This article is meant as a first-pass introduction to artificial neural networks. The goal is not formula-heavy detail, but a workable mental model of what the layers do, why the network can learn, and where different neural network types are commonly applied.

If you plan to continue into deep learning, this structure-first approach is often easier than jumping straight into equations.