Input Layer

The first layer of a neural network that receives data before it is processed by other layers.

Overview

Every neural network begins with information entering the system.

The component responsible for receiving that information is called the input layer.

The input layer is the first layer of a neural network.

Its role is to accept data and pass it to subsequent layers for processing.

Each input neuron typically represents a feature or variable from the dataset.

For example, a model predicting house prices might receive inputs such as location, square footage, and number of bedrooms.

The input layer itself performs little computation.

Instead, it serves as the entry point through which information enters the network.

From there, hidden layers process the information and eventually produce an output.

Understanding the input layer helps explain how neural networks transform raw data into meaningful predictions and decisions.

The input layer provides the foundation upon which the entire neural network operates.

An image recognition model receives pixel information through the input layer before analyzing the image.