MATLAB Implementation for Training Backpropagation Neural Networks with Customizable Architecture

This MATLAB implementation provides a robust framework for training backpropagation neural networks using gradient descent optimization. The code structure employs matrix operations for efficient forward and backward propagation, leveraging MATLAB's built-in linear algebra capabilities. Key components include layer initialization with customizable neuron counts, activation function selection (sigmoid, ReLU, or tanh), and batch processing for memory-efficient training. The implementation features adjustable hyperparameters including learning rate schedules, momentum coefficients for gradient acceleration, and early stopping criteria based on validation set performance. The training algorithm computes error gradients through chain rule differentiation, updating weights and biases iteratively to minimize loss functions like mean squared error or cross-entropy. Users can configure network architecture through parameter arrays specifying hidden layer dimensions, while the core training loop implements weight updates using derivative calculations and learning rate adjustments. The code includes data normalization routines, weight initialization strategies (Xavier/Glorot), and visualization tools for monitoring training progress through loss convergence plots. Advanced features support regularization techniques (L2 penalty), gradient clipping for stability, and mini-batch processing for large datasets. The modular design allows easy integration of custom activation functions or optimization algorithms while maintaining computational efficiency through vectorized operations across training examples.