Double Hidden Layer Backpropagation Neural Network Routine

This routine demonstrates a double hidden layer backpropagation neural network designed specifically for educational purposes, with clear explanations of the underlying algorithm. The implementation includes the following key aspects: - Neural networks are mathematical models that mimic the interconnected communication between neurons in the human brain. In code implementation, this typically involves creating layer objects with interconnected nodes and weight matrices. - The backpropagation algorithm is a common training method for neural networks that works by continuously adjusting weights and biases through gradient descent optimization. The implementation typically involves forward propagation to calculate outputs, followed by backward propagation to compute error gradients using chain rule differentiation. - A double hidden layer neural network architecture contains two hidden layers between the input and output layers, enabling better capture of complex relationships in input data. The code structure usually includes initialization of two separate weight matrices and bias vectors for the hidden layers, with activation functions like sigmoid or ReLU applied between layers. This routine aims to help users better understand double hidden layer backpropagation neural networks and serve as a valuable teaching aid. The code implementation demonstrates proper initialization, forward propagation, error calculation, and weight update procedures essential for neural network training.