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- Move compression to 10 (right after training - model optimization) - Move mlops to 11 (deployment comes next - production readiness) - Move profiling to 12 (optimization tools - when needed) - Move benchmarking to 13 (comparative analysis - with profiling) - Move kernels to 14 (advanced optimization) - Move transformer to 15 (final advanced topic) Better Learning Flow: - Training → 'I have a model, now what?' → Deployment - Deployment → 'Now let me optimize' → Profiling/Benchmarking - Mirrors real ML engineering: Deploy first, optimize second - Profiling + Benchmarking work together naturally Final progression (00-15): 00_setup → 01_tensor → 02_activations → 03_layers → 04_networks → 05_dataloader → 06_cnn → 07_autograd → 08_optimizers → 09_training → 10_compression → 11_mlops → 12_profiling → 13_benchmarking → 14_kernels → 15_transformer