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Multilayer Perceptron (MLP) and Its Architecture

A Multilayer Perceptron (MLP) is a type of artificial neural network (ANN) that consists of multiple layers of neurons, making it a powerful tool for classification, regression, and function approximation. It is a fully connected feedforward network that learns complex patterns through training.

MLP Architecture

MLP consists of three main layers:

1. Input Layer

   • Receives raw input features $X$.

   • Each neuron represents one feature, and no computations are performed here.

2. Hidden Layers (One or More)

   • Each neuron applies a weighted sum followed by a non-linear activation function (e.g., ReLU, Sigmoid, Tanh).

   • Helps the network learn complex patterns.

3. Output Layer

   • Produces final predictions.

   • Uses activation functions based on task type:

     • Sigmoid/Softmax for classification.

     • Linear for regression.

Training an MLP

MLP is trained using Supervised Learning, following these steps:

1. Forward Propagation

   • Inputs pass through the network, generating outputs.

2. Loss Calculation

   • The difference between predicted and actual outputs is measured using a loss function (e.g., Cross-Entropy for classification, Mean Squared Error for regression).

3. Backpropagation

   • Computes gradients using chain rule to update weights.

4. Weight Update

   • Uses optimization algorithms like Stochastic Gradient Descent (SGD) or Adam to minimize loss.

Applications of MLP

• Image and speech recognition

• Financial forecasting

• Natural language processing (NLP)

MLPs are fundamental in deep learning, serving as the basis for more advanced networks like CNNs and RNNs.