1 Match the definition to the correct neural network layer. Output Layer N Input Layer Match each of the options above to the items below. 3 لیا Hidden Layer Data and information from a variety of sources is entered into the neural network through this layer. This layer processes data sent by the input layer or from other associated layers. In this layer, information from the previous layers is analyzed. After this analysis occurs, a final prediction or conclusion is made.
The Correct Answer and Explanation is:
Correct Matches:
- Input Layer – Data and information from a variety of sources is entered into the neural network through this layer.
- Hidden Layer – This layer processes data sent by the input layer or from other associated layers. In this layer, information from the previous layers is analyzed.
- Output Layer – After this analysis occurs, a final prediction or conclusion is made.
Explanation):
In an artificial neural network, the architecture is composed of three main types of layers: input, hidden, and output layers. Each plays a unique and essential role in how the network learns and makes predictions.
The input layer is the first layer of the network. It is responsible for receiving raw data from the outside world, such as images, text, or numerical values. This layer does not perform any computations; instead, it acts as a conduit, feeding the data into the next layer of the network for processing. For example, if the task is image recognition, each input neuron might represent a pixel’s brightness value.
The hidden layers are the core of the neural network where computation and learning occur. These layers perform complex transformations and feature extraction on the input data. Using weighted connections and activation functions, the hidden layers detect patterns, relationships, and high-level features in the data. Deep neural networks have multiple hidden layers, which allow them to learn increasingly abstract representations at each level.
Finally, the output layer delivers the final result of the network’s processing. It aggregates the learned information from the last hidden layer and makes a prediction or decision based on the network’s purpose. For classification tasks, the output layer may have multiple neurons (one for each class), and its values often go through a softmax function to represent probabilities. For regression tasks, there might be only one output neuron providing a continuous value.
Together, these three layers enable neural networks to model and solve complex problems by mimicking the way human brains process information—making sense of inputs, identifying patterns, and generating meaningful outputs.
