Multilayer Perceptron (MLP)

What is a Multilayer Perceptron (MLP)?

A Multilayer Perceptron (MLP) is a type of artificial neural network composed of multiple layers of nodes or neurons. MLPs are feedforward networks, meaning that data travels in one direction from the input layer through one or more hidden layers to the output layer. MLPs are used for a variety of tasks, including regression and classification problems. They are capable of learning complex patterns and nonlinear relationships between input features and output targets.

How does a Multilayer Perceptron work?

An MLP consists of an input layer, one or more hidden layers, and an output layer. Each layer contains a set of nodes, and each node is connected to nodes in the subsequent layer through weighted connections. During the training phase, the MLP learns the optimal weights for these connections using a process called backpropagation, which minimizes the error between the predicted outputs and the actual targets. Once the model is trained, it can be used to make predictions on new data.

Resources for learning more about Multilayer Perceptrons:

  1. Multi layer perceptron - a detailed explanation of MLP with a real-life example and Python code for sentiment analysis.

  2. Crash course on Multi layer Perceptron - a comprehensive guide to understanding MLPs and their implementation.

  3. TensorFlow’s Multilayer Perceptron (MLP) tutorial - a step-by-step guide to implementing and training an MLP using TensorFlow.