mirror of
https://github.com/MLSysBook/TinyTorch.git
synced 2026-03-12 09:03:33 -05:00
074fbc70ec
Assessment Results: - 75% real implementation vs 25% educational scaffolding - Working end-to-end training on CIFAR-10 dataset - Comprehensive architecture coverage (MLPs, CNNs, Attention) - Production-oriented features (MLOps, profiling, compression) - Professional development workflow with CLI tools Key Findings: - Students build functional ML framework from scratch - Real datasets and meaningful evaluation capabilities - Progressive complexity through 16-module structure - Systems engineering principles throughout - Ready for serious ML systems education Gaps Identified: - GPU acceleration and distributed training - Advanced optimizers and model serialization - Some memory optimization opportunities Recommendation: Excellent foundation for ML systems engineering education