math-modeling-pipeline/phase-5-dl-matlab

What problem does it solve?

Phase 5-DL-MATLAB targets optimizing MATLAB deep learning models after cross-review, refining architecture, training options, and data augmentation to improve performance and reproducibility.

Core Features & Use Cases

• Architecture optimization: add residual blocks, attention mechanisms, and DAG networks for better accuracy.
• Training and augmentation: advanced training options, cyclic learning rate schedules, and robust data augmentation.
• Evaluation & logging: produce convergence reports, comparison metrics, and logs for audit.
• Use Case: A team iterates on a MATLAB-based DL model to push PSNR/SSIM improvements and faster convergence across multiple datasets.

Quick Start

Run the Phase 5-DL-MATLAB optimization workflow to produce optimized MATLAB DL models and a convergence report.