Big data ML best practice

September 16, 2020 — September 21, 2020

Figure 1

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.

Figure 2: Abstruse Goose says

The Turing Way by the Alan Turing institute covers many reproducible research/open notebook science ideas which includes some tips applicable to ML research.

Figure 3: Jordan’s test-ML pipeline

1 Tools

gin-config configures default parameters in a useful way for ML experiments.

2 Incoming

3 References