{"id":7002,"date":"2024-01-17T16:01:04","date_gmt":"2024-01-17T15:01:04","guid":{"rendered":"https:\/\/www.artea.com\/what-are-neural-networks\/"},"modified":"2026-09-29T09:29:54","modified_gmt":"2026-09-29T07:29:54","slug":"what-are-neural-networks","status":"publish","type":"post","link":"https:\/\/www.artea.com\/en\/what-are-neural-networks\/","title":{"rendered":"WHAT ARE NEURAL NETWORKS?"},"content":{"rendered":"\t\t
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Neural networks<\/strong> form the backbone of machine learning and deep learning, two rapidly growing fields that are revolutionizing the way machines interpret data and reality.<\/p>

This technology is inspired by the complexity and versatility of the biological neurons that make up the human brain. But how exactly do they work? <\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t

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The Pillars of Learning: Structure and Function<\/h2>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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A neural network consists of processing units called “nodes<\/strong>, “<\/strong> which are interconnected in a complex structure that mimics the brain’s neural circuits. These nodes receive, process, and transmit signals in the form of data. <\/p>

During processing, each node performs a linear regression<\/strong> model\u2014a statistical method used to understand the relationship between two variables. It then performs a nonlinear transformation<\/strong>\u2014a mathematical rule that converts an input into an output in a way that is not directly proportional. Let\u2019s take a closer look at these two steps. <\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t

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Linear Regression at Nodes<\/h3>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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In In a neural network, each node receives one or more inputs. These inputs can be the raw data fed into the network or the output from other nodes. Each input is associated with <\/span>a weight<\/b>, which represents the relative importance of that input to the node. The node calculates a weighted sum of its inputs, which is essentially a linear regression<\/strong> calculation. A bias<\/strong> is then added, allowing the node to adjust its activation level.
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Step 2: Activation Function and Nonlinearity<\/h3>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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After calculating this weighted sum, the node applies an activation function to its output. This function is of fundamental importance because it introduces nonlinearity into the model. Without it, even if we had multiple layers of nodes, the neural network would only be able to learn linear relationships between the input and output data, significantly limiting its power. <\/p>

The combination of linear regression and an activation function allows each node to make predictions and decisions based on the data it receives and to pass this information along the network.<\/p>

During the training process<\/strong>, the neural network continuously improves its performance by correcting errors. This is achieved by adjusting the weights and biases of each node (a process known as backpropagation). During training, when the network makes a prediction that turns out to be incorrect, the weights and biases are modified to reduce the error. This continuous refinement allows the network to effectively learn to perform complex tasks, improving its accuracy over time. <\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t

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From Input to Decision: A Concrete Example<\/h2>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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To illustrate how neural networks make complex decisions, let\u2019s consider the example of a neural network system used for vehicle recognition in traffic images. This system must determine whether or not a vehicle is present in a given image and, if so, what type of vehicle it is. <\/p>