ml-eng-lab
A hands-on PyTorch notebook portfolio where each notebook runs end to end and is CI-validated via NNx — spanning classical ML, GNNs, NLP, transformer LMs with BPE, DDPM diffusion, DPO, JEPA, MoE, PEFT, quantization, pruning, and knowledge distillation.
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Per-day rates divide a measured star count by the days it covers: the interval between two snapshots for Rising fastest, the age of the repository for New and fast. They are not fixed 24-hour changes, and stars show interest, not quality.