DBN Implementation Example: Building a Two-Hidden-Layer Neural Network

This example demonstrates constructing a neural network with two hidden layers, featuring 500 input dimensions and 10 output dimensions. The hidden layers contain 50 and 20 nodes respectively, with a final softmax layer to compress outputs into probabilistic values. The implementation covers network architecture, loss function selection, and optimization algorithms.

RBF Neural Network Classifier with 4-Input Layer, 3-Hidden Layer, and 2-Output Layer Architecture

MATLAB implementation of a Radial Basis Function (RBF) neural network classifier featuring a 4-3-2 layer configuration, complete with training and testing datasets for performance evaluation