Big data ML best practice
September 16, 2020 — September 21, 2020
A grab bag of links I have found pragmatically useful in the topsy-turvy world of ML research. Here, where even though we have big data about the world, we still have small data about our own experimental models of the world, because they are so computationally expensive.
see also Surrogate optimisation of experiments.
Martin Zinkervich’s Rules of ML for engineers, and Google’s broad brush workflow overview. Andrej Karpathy’s Recipe for training neural networks. Zayd Enam on why debugging machine learning is hard.
Jeremy Jordan on writing tests for ML.
The Turing Way by the Alan Turing institute covers many reproducible research/open notebook science ideas which includes some tips applicable to ML research.
1 Tools
gin-config configures default parameters in a useful way for ML experiments.
2 Incoming
- Normconf Lightning Talks/Normconf: The Normcore Tech Conference — a conference on the stuff that we actually need to do in ML, as opp. the stuff we would like to pretend is what we do.
- Kedro | A Python framework for creating data science code /Kedro Frequently asked questions. Kedro rationale by Joel Schwarzmann: The importance of layered thinking in data engineering