My PyTorch neural network keeps returning unexpected, incorrect predictions on a numerical dataset. I need a fresh set of eyes to trace the root cause and get the model performing as intended. Scope of work • Inspect the full training pipeline—data loading, preprocessing (NumPy/pandas), model architecture, loss function, optimizer, and scheduler. • Pinpoint why convergence stalls and outputs diverge from the expected range. • Apply and document fixes: code corrections, hyper-parameter tweaks, or architectural adjustments. • Re-train and validate the model, providing before-and-after metrics plus example predictions to prove the improvement. • Deliver clean, commented code, a concise troubleshooting report, and recommendations for future tuning or scaling (GPU/CUDA tips welcome). I’ll share the repository, sample data, and current evaluation script. A solid grasp of PyTorch debugging tools (e.g., hooks, gradient inspection) and good communication during testing rounds are essential. Once the predictions align with the ground-truth targets to an agreed threshold, the job is done.