Neural Network Prediction Source Code with Implementation Guide

This neural network prediction source code package provides detailed documentation accompanied by visual illustrations. The implementation includes core algorithm details such as forward/backward propagation mechanisms, activation function configurations (ReLU, sigmoid, or tanh), and weight optimization methods like gradient descent or Adam optimization. The code structure demonstrates layer initialization, training loop implementation, and prediction logic with configurable parameters for hidden layers and learning rates. Additional technical content covers recent advancements in deep learning architectures, hyperparameter tuning strategies, and performance evaluation metrics (e.g., MSE, accuracy scores). Users can adapt the modular codebase for custom datasets by modifying input/output dimensions and integrating specialized layers (CNN/LSTM modules). This resource enables deeper understanding of neural network prediction principles while providing extensible foundation for research or production applications.