Neural networks now play a central role in artificial intelligence. Unlike linear or logistic regression, which are simple predictive models designed to identify direct relationships between variables, neural networks are built to capture much more complex patterns.

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An artificial neural network is inspired by the principle of biological neurons, but it does not replicate the functioning of the human brain, which science still does not fully understand. It is a simplified mathematical representation where data passes through a succession of “layers.” Each layer performs a basic transformation, extracts part of the useful information, and then passes the signal on to the next. This chain of progressive processing allows the model to learn increasingly complex structures.
In business, neural networks are especially valuable when data is large, heterogeneous, and difficult to interpret with traditional approaches such as images, videos, text, or user behavior. These sources often contain valuable signals about market dynamics, customer expectations, or product quality.

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They are also effective when relationships between variables are particularly complex and cannot be captured by simple predictive models like linear regression. In such situations, neural networks highlight subtle regularities and significantly improve prediction accuracy in rich, multidimensional environments.

How neural networks work ?

The functioning of a neural network can be explained without delving into mathematical detail. The central idea is that each input passes through a series of transformation steps. These steps are made up of “artificial neurons,” which are simply computational units. Each one assigns a weight to the information it receives and transmits a transformed version of the signal to the next.

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Initially, these weights are set randomly. The model then learns by comparing its predictions with actual outcomes: whenever an error is detected, the internal weights are adjusted to reduce the gap. Through many iterations, the network gradually identifies the combinations of factors that yield the most reliable predictions.
This process differs from linear regression. In linear regression, the relationship is represented by a clear trend line, which decision-makers can easily interpret. In a neural network, the relationship is more complex, built from multiple layers of interactions. The explanation becomes less transparent, but the ability to model non-linear phenomena is much greater.

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Another advantage lies in the diversity of possible architectures. Some networks are relatively simple, designed to handle straightforward classification or estimation problems. Others, composed of many layers, can capture very complex interactions and process rich data such as images, videos, or text.

One of the main advantages of neural networks is their ability to leverage complex data that traditional models struggle to analyze. They can identify patterns in information such as images, videos, text, or audio signals and transform them into useful operational predictions. This ability to handle rich data opens the door to new use cases, from visual recognition to personalized customer experiences.

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However, neural networks also present a major limitation for businesses: their lack of interpretability. Unlike linear or logistic regression, where each coefficient can be analyzed to understand the influence of a variable, neural networks rely on thousands or even millions of intertwined parameters. The result is often described as a “black box”: the model provides a reliable prediction, but it is difficult to explain exactly how it reached that result. This opacity can be a barrier for decision-makers who expect clear justifications to guide their strategic choices.

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