Artificial intelligence agents, computer vision, generativeAI: in manyartificial intelligenceapplications, feedforwardneural networksplay a crucial role. Among the types ofartificial neural networks, they are the most fundamental, serving as the foundation for more complex architectures.

In free translation,Feedforwardmeans "nurturing the future" or "feeding to advance." Applied to systems, what this means is that the flow of information is always forward, moving in a single direction, from input to output. In Feedforwardneural networks, data always passes to the next layer, without feedback.

FNNs – short for Feedforward Neural Networks – are atthehistorical origins of the use ofartificial intelligence, but still make up the architecture of some of today's most advancedAImodels. Follow along with us in this text to understand how they work and what their most common uses are!

Layered architecture

Artificial neural networksare computational models, types of application architectures. They have two main purposes: to process information and to learn from the processing itself. In other words, as data is processed, it influences the considerations for the next stage of processing until the final result is reached.

What we have just called "stages" can be replaced by layers. AnRNA, or artificial neural network, is built with at least three layers: an input layer, a hidden processing layer, and an output layer. Each of these has severalperceptrons, the classifiers that function as the network's "neurons."

In everyRNA, there is a flow of information that moves forward. The difference between feedforwardneural networksand other types is that they only have forward flow, with no feedback. In FNNs, backpropagation may occur, but this is different from feedback in the strict sense, which is typical of recurrentneural networks.

In practice, it works like this: the perceptrons in the input layer receive the values and pass them on to those in the processing layer. They calculate the weighted sum taking into account the values received, the weights of each of them, and a bias value. If theANNhas more than one hidden layer, the result of the calculation of each perceptron in the previous layer is the input value for each of the following layers, and so on, until the output layer.

In the first Feedforwardneural networks, learning took place thanks to the adjustment of weights in each layer. However, this level of learning had limitations, since when an error occurred, it was not taken into account to adjust the processing. This gap was corrected with the backpropagation algorithm, described in thearticle "Learning representations through error backpropagation."

Thus, in modern multilayerartificial neural networks, the error is “backpropagated”—propagated backward to the previous layers—to determine how much each weight and bias contributed to it. In other words, by analyzing the error, the application learns much more consistently.

Feedforward neural networks: most common uses

The main capability of FNNs is to act as universal approximators, making them ideal for recognizing and classifying patterns. Consequently, they can perform data regression with high accuracy, allowing them to make predictions based on data analysis. This level of processing is necessary for solving various types of problems; that is why Feedforwardneural networksare the basis of anyRNA architecture, as we said at the beginning of this text.

MLPs (MultiLayer Perceptron), responsible for natural language processing, are currently the most common type of FNN. The ability to process language has enabled the existence of LLMs, such as theartificial intelligence agentsin the form of chatbots that we use daily. In general, they have aneural networkarchitecture known asTransformer, a hybrid type ofRNA; however, their characteristics bring them closer to a Feedforward neural network than to a recurrent network.

Another type of FNN, more specialized, are convolutional networks. They are widely used in computer vision, mainly in the recognition of objects in images, and also in the recognition of other types of unstructured data, such as audio.

There are many possibilities for usingapplicationswithartificial intelligence, whether or not they use feedforwardneural networks. One reality we have experienced here at Inmetrics is projects tomodernize applicationsto make them compatible withthe change from CNPJto thealphanumeric model. Our solution usesartificial intelligenceto update and validate masks, adjust APIs, integrate systems, and even help usconvert code.

If you’re facing other types of challenges at your company and thinkAIcan help,contact us to learn how we integrate it into all our solutions!

Artificial neural networks, whether FNN or other types, may be the fastest way for you to overcome technological or business bottlenecks in your reality.Click here, contact us, and talk to one of our experts about your challenges; we are confident that our team will develop the best solutions to accelerate yourtransformation!