In the field of Artificial Intelligence, Hidden Layers represent the core of artificial neural networks. Positioned between the initial input and the final output, they process the information received as input in order to produce the output. The presence of these layers is precisely what defines Deep Learning, a subcategory of Machine Learning that enables extremely complex tasks to be performed through AI systems.

A Deep Learning system is a neural network composed of numerous layers and capable of modeling complex relationships. But what exactly is a neural network? A neural network is a Machine Learning program that makes decisions by using processes that imitate the way biological neurons in the human brain work together to identify phenomena, weigh options, and reach conclusions. Just as the human brain is composed of motor neurons that depend on the sensory neurons to which they are connected, an artificial neural network is likewise composed of artificial neurons that form interconnected networks. More specifically, it consists of input neurons, output neurons, and hidden neurons, that is, hidden neurons. A numerical value is assigned to the input neurons, which belong to the first layer, so that this layer, as a whole, encodes the information on the basis of which a decision must be made. Once the nodes in the first layer have been assigned a numerical value, they pass the information associated with that value to the hidden layers. The hidden, or intermediate, layers perform calculations on the information received from the input neuron layer and transfer the result to the final layer, that of the output neurons, which represents an encoding of the calculation’s result, that is, the final decision.

Each neuron is connected to every other neuron in the subsequent layer, and each connection between neurons has a weight. Weights are the parameters that determine the importance of each input in calculating the output. In practice, weights are the coefficients that multiply the associated input, thereby influencing that input’s contribution to the neuron’s overall output. The weight of each individual connection constitutes the fundamental element that is modified during training. Training consists of a process in which the model is exposed to a vast amount of data (examples), from which it autonomously learns, through experience, to recognize specific patterns and relationships between inputs and expected results. Through this continuous processing, the system optimizes its performance and reduces the margin of error by constantly adjusting the network’s internal parameters until it produces the desired decisions or classifications. By modifying the weights, the neural network can learn how to recognize patterns in data and make accurate predictions.

Another element affected by training is the bias. Bias is a constant value that is added to the calculation of the weighted inputs once a neuron has received inputs from all the other neurons connected to it. Bias is also modified during training. By adjusting the bias, the network can learn to make more accurate predictions and adapt better to the training data. By adjusting weights and biases, a neural network is able to reach a configuration that allows it to address a real-world decision-making problem and produce acceptable decisions. The adjustment of weights and biases, and their use, take place precisely in the part of the network between the input layer and the output layer, that is, in the hidden layer.

The functioning of Deep Learning systems relies on a vast amount of data and, when the goal is to address highly complexproblems, on networks that can have up to hundreds of layers. As a result, it is only recently, thanks to the exponential increase in computational capacity and the greater availability of data, that the systematic use of AI models of this kind has begun.

Gaia Vilma Polce

(translated by MIRAI)