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Supplementary Materials

Practical skills that every ML engineer needs but do not fit neatly into the other chapters.

  • Python Tips -- Generators, context managers, dataclasses, type hints, performance
  • Linux and Shell -- Essential commands, environment management, SSH, SLURM
  • Git for ML -- Branching for experiments, git lfs, DVC