Submitted by laprika0 t3_yj5xkp in MachineLearning
suflaj t1_ium3372 wrote
ML is easy since it's mostly on the CPU. DL still remains shit, unless your definition of prototyping is verifying that the shapes match and that the network can do backprop and save weights at the end of an epoch.
Things are not going to change fast unless Macs start coming with Nvidia CUDA capable GPUs.
laprika0 OP t1_ium613q wrote
Thanks. You differentiate ML from DL. Can you say what you mean by that in this context? Is working with DL a different experience than working with e.g. probabilistic modelling? Or do you mean e.g. tensorflow, pytorch, jax vs pandas, numpy, scikit-learn?
TheDeviousPanda t1_ium7iy4 wrote
Scikit learn numpy pandas xgboost etc, totally fine to do on CPU which is great on MacBooks. Pytorch tensorflow jax? Forget it. If anyone in lab asks for help debugging on their local machine bc cluster is down, I just ignore it. Impossible to do prototyping on Mac.
suflaj t1_ium7xcv wrote
ML is a superset of DL. It's very different working on those two, almost like most ML rules and theory straight up do not apply to modern DL.
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