MLP (Multi-Layer Perceptron)

Information

• MLP (Multi-Layer Perceptron) is a type of feedforward artificial neural network (ANN) that consists of multiple layers of nodes.
• It is composed of an input layer, one or more hidden layers, and an output layer.
• Each node in a layer is connected to every node in the next layer, forming a dense graph.
• MLP is trained using backpropagation, a supervised learning technique that adjusts the weights of the connections between the nodes to minimize the error between the network's predicted output and the true output.
• MLP is a universal approximator, meaning that it can approximate any continuous function given a sufficient number of hidden nodes.
• It is often used for regression and classification tasks, including time series forecasting, image recognition, and natural language processing.
• MLP can suffer from overfitting, a common problem in machine learning where the model performs well on the training data but poorly on new data, and regularization techniques like L1 and L2 regularization can be used to prevent this.
• The number of hidden layers and nodes, as well as the activation functions used in each layer, are hyperparameters that need to be tuned to achieve optimal performance.