Simone Raponi : 12 November 2025 21:58
In the contemporary technological landscape, one term stands out: neural network. The foundation of artificial intelligence, neural networks represent the future of how we interact with technology. But what exactly is a neural network?
Let’s start our journey.
A neural network is a computational model inspired by the functioning of the human brain . This similarity is not limited to a simple metaphor: just as our brain is composed of interconnected neurons, a neural network is composed of computing units, or “artificial neurons,” organized into different layers.
Each neuron receives input data, performs a series of calculations on this data, and provides an output result, which can be used as input for neurons in the next layer.
Neural networks are the foundation of many artificial intelligence applications. Their ability to learn from data and improve their performance over time makes them a fundamental tool in numerous fields, from computer vision to machine translation to data prediction.
Neural networks learn through a process known as “training.” This process can be divided into two main phases: the forward propagation phase and the back propagation phase.
During the forward propagation phase, the neural network receives input data and , through a series of calculations, generates a prediction or output . For example, imagine a neural network trying to identify images of cats. The input will be a set of pixels representing the image, and the neural network will try to process this data to determine whether the image represents a cat or not.
In each neuron, the input is combined with a weight (representing the relative importance of that input) and then run through an activation function , which determines the neuron’s output.
Once the output is generated, the backpropagation phase comes into play. During backpropagation, the neural network compares the generated output with the desired output (i.e., the correct answer). In our example, if the neural network correctly identified the image as a cat, then the error will be low; if it incorrectly identified the image, then the error will be high.
This error is then used to adjust the weights of each neuron , thus reducing the error in future predictions . Our system thus becomes more effective at recognizing cats in future images.
This cycle of forward propagation and back propagation continues for a number of iterations , or “epochs,” until the neural network is no longer able to significantly improve its predictions.
One aspect worth noting is that training a neural network requires a large amount of data and computing resources. This can make training neural networks a challenging task, especially for very deep networks or for very complex problems.
For a more technical analysis, I recommend reading Michael Nielsen’s excellent article .
By exploring neural networks, we’ve entered a fascinating universe where technology seeks to emulate the functioning of the human brain to solve complex problems. These powerful machine learning tools are opening previously unimaginable doors, revolutionizing fields ranging from computer vision to machine translation, from medical diagnosis to autonomous vehicle driving.
It’s true that training a neural network requires considerable resources and a large amount of data. It’s also true that implementing these technologies presents daunting challenges, requiring a thorough understanding of both the neural network model and the data being worked with. But there’s no innovation without challenge, no progress without commitment.
Neural networks, with their astonishing ability to learn and adapt, represent the cutting edge of Artificial Intelligence. We are only just beginning to grasp the incredible potential of this technology, and the road to the future promises to be full of revolutionary discoveries.
