Neural Network Model Implementation

This code implements a neural network model that can be customized by adjusting parameters and architecture to meet various requirements. Users can experiment with architectural modifications such as adding more hidden layers to increase model depth or adjusting the number of nodes per layer to alter model complexity. Key implementation aspects include selecting appropriate activation functions (e.g., ReLU, sigmoid, tanh), loss functions (e.g., cross-entropy, MSE), and optimization algorithms (e.g., Adam, SGD) to enhance model performance. The training process involves forward propagation through multiple layers and backward propagation for gradient calculation. By continuously refining the model architecture and hyperparameters through iterative testing, users can achieve improved prediction accuracy and adapt the model to different application scenarios. The code structure allows straightforward modifications to layer configurations, regularization techniques, and training parameters for optimal results.