AI neural network glowing nodes and connections in 3D space

Artificial neural networks, with glowing nodes and connections in 3D space, represent the computational architecture driving deep learning.

About this subject

Artificial neural networks are computational models inspired by the human brain, composed of layers of interconnected nodes (neurons). Each connection has an adjustable weight, allowing the network to learn patterns from data. The concept emerged in the 1950s, but it was the advancement of computing power and large datasets that drove its explosive use from 2010 onward.

In practice, a typical neural network has an input layer, one or more hidden layers, and an output layer. During training, weights are adjusted via backpropagation, minimizing the error between predicted and actual output. Deep networks with many hidden layers can extract hierarchies of features, from simple edges in images to abstract concepts like faces or objects.

Applications include image and speech recognition, machine translation, autonomous vehicles, and medical diagnosis. In Brazil, institutions such as the Institute of Mathematical and Computer Sciences (ICMC-USP) and the National Laboratory for Scientific Computing (LNCC) research neural networks for national problems, like crop forecasting and SUS data analysis.

Trivia: the term "deep learning" was popularized in 2006 by Geoffrey Hinton, but the first functional deep neural network dates to 1965, created by Alexey Ivakhnenko and Valentin Lapa in Ukraine.

Frequently Asked Questions

What is an artificial neural network?

It is a computational model inspired by the brain, composed of layers of interconnected nodes that learn patterns from data by adjusting connection weights.

What is the difference between neural network and deep learning?

Deep learning is a subset of neural networks with many hidden layers (deep networks), capable of learning complex hierarchical representations.

Where are neural networks applied in Brazil?

In image recognition, natural language processing, agricultural crop forecasting, public health data analysis, and recommendation systems.