Backpropagation Multi-Layer Perceptron (MLP) Neural Network

Backpropagation, Multi-Layer Perceptron (MLP), Neural Network

Backpropagation is a fundamental learning algorithm for neural networks, typically implemented using Multi-Layer Perceptrons (MLP). The algorithm employs error backpropagation to minimize the difference between the neural network's output and the target output. This method iteratively adjusts the weights and biases of the network to enhance its performance through gradient descent optimization.

Implementation typically involves forward propagation to compute outputs, followed by backward propagation of errors using chain rule differentiation. Key functions include calculating the loss gradient with respect to each weight matrix, updating parameters using optimization techniques like stochastic gradient descent, and applying activation functions (sigmoid, ReLU, tanh) at each layer. The algorithm requires careful tuning of hyperparameters such as learning rate, batch size, and number of hidden layers to prevent overfitting and ensure convergence.