Artificial Neural Network Design Example

I have successfully implemented and tested an artificial neural network design example with verified correct output. The implementation demonstrates key aspects of neural network architecture including layer configuration, neuron count per layer, and activation function selection (such as ReLU, sigmoid, or tanh). The example also incorporates optimization algorithms like gradient descent with backpropagation for weight updates, featuring learning rate tuning and convergence monitoring. Further discussions cover practical considerations for neural network design including parameter initialization techniques, batch normalization implementation, and methods to prevent overfitting through regularization approaches. The code structure typically involves defining the network architecture using frameworks like TensorFlow or PyTorch, implementing forward propagation for prediction, and backward propagation for gradient calculation during training cycles.