ANN Series - 1 - Introduction and Components

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An Artificial Neural Network (ANN) is a computational model inspired by the networks of biological neurons found in animal brains. It is a cornerstone of artificial intelligence (AI) and machine learning, designed to simulate the way a human brain analyzes and processes information. ANNs are capable of learning and making intelligent decisions on their own by recognizing patterns in data.

Structure of an ANN

An ANN is composed of nodes (also called neurons) arranged in layers: an input layer, one or more hidden layers, and an output layer. Each node in one layer is connected to nodes in the next layer through pathways that are associated with weights. These weights adjust as the network learns from data, essentially tuning the ANN to perform a specific task.

Structure of an ANN

An ANN is composed of nodes (also called neurons) arranged in layers: an input layer, one or more hidden layers, and an output layer. Each node in one layer is connected to nodes in the next layer through pathways that are associated with weights. These weights adjust as the network learns from data, essentially tuning the ANN to perform a specific task.

  • Input Layer: Receives the input signals (data) and passes them to the next layer.
  • Hidden Layers: Perform computations and feature extractions. The complexity of the model depends on the number of hidden layers and the nodes within them.
  • Output Layer: Delivers the final output generated by the network, such as a classification or prediction.

Single Layered Networks

They are composed of only the input and output layers.

How ANNs Learn

The learning process in an ANN involves adjusting the weights of the connections based on the error of the output compared to the expected result. This process is typically performed using an algorithm called backpropagation, combined with an optimization technique such as gradient descent. The goal is to minimize the error (or loss) function, making the network's predictions as accurate as possible.


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