Machine Learning - Simplified

The terms "Multilayer Perceptron" (MLP) and "Multilayer Artificial Neural Network" (Multi-layered ANN) are often used interchangeably to describe a specific type of neural network architecture. Both refer to a class of artificial neural networks that contain one input layer, one or more hidden layers, and one output layer.  The key characteristics that define MLPs and multi-layered ANNs include: 1. Multiple Layers:  Both MLPs and multi-layered ANNs are characterized by having multiple layers of neurons (also known as nodes). These include the input layer that receives the data, one or more hidden layers that process the data, and the output layer that produces the final prediction or classification. 2. Non-linear Activation Functions:  In both architectures, neurons in the hidden layers and sometimes in the output layer apply non-linear activation functions to their inputs. These functions enable the network to learn complex patterns and relationships in the data th...