Supervised Neural Network models

A supervised neural network model is a type of machine learning model used for tasks where you have labelled data, meaning you know both the input and the corresponding correct output. In this model, you feed input data into layers of interconnected artificial neurons, which process the information and produce an output. During training, the model learns to adjust its internal parameters (weights and biases) to minimize the difference between its predictions and the actual labels in the training data. This process continues until the model can make accurate predictions on new, unseen data. Supervised neural networks are commonly used for tasks like image classification, speech recognition, and natural language processing, where the goal is to map inputs to specific categories or values.

An artificial neural network (ANN), often known as a neural network or simply a neural net, is a machine learning model that takes its cues from the structure and operation of the human brain. It is a key element in machine learning’s branch known as deep learning. Interconnected nodes, also referred to as artificial neurons or perceptrons, are arranged in layers to form neural networks. An input layer, one or more hidden layers, and an output layer are examples of these layers. A neural network’s individual neurons each execute a weighted sum of their inputs, apply an activation function to the sum, and then generate an output. The architecture of the network, including the number of layers and neurons in each layer, might vary significantly depending on the particular task at hand. Several machine learning tasks, such as classification, regression, image recognition, natural language processing, and others, can be performed using neural networks because of their great degree of versatility.

In order to reduce the discrepancy between expected and actual outputs, a neural network must be trained by changing the weights of its connections. Optimization techniques like gradient descent are used to do this. In particular, deep neural networks have made significant advances in fields like computer vision, speech recognition, and autonomous driving. Neural networks have demonstrated an exceptional ability to resolve complicated issues. They play a key role in modern AI and machine learning due to their capacity to automatically learn and extract features from data.