20 years ago, I created the Danish gaming site Daily Rush with @mwittrock – inside a startup accelerator called Prey4, complete with fantastical projections of world domination 😂 – but now it's the end, after the proprietor of many years died in 2018.

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Today 35000CE and 34500PE

THREAD

I get asked for writing tips a lot. Of all the ones I’ve given, here are a few of the ones that seem to hold true and remain somewhat universal or at least mostly unobjectionable:

#amwriting #writingtips

1. If you are a fast writer (i.e., if you compose first drafts very quickly) then you absolutely must become a slow editor. Much of what I call “writing” is really, for me, editing.

2. If you don’t read you cannot write a lot or well. Sure, maybe you can read like a maniac for a decade and then read less after that, but without some large volume of intake, there will never be a meaningful output.

2.1 I sometimes say that I feel like writing without reading is like trying to run a marathan on a cracker. And sadly I see many students struggling to write when they have simply never read enough to fuel that demand of the task at hand.

3. All writing is writing if one decides to treat it that way. If one takes some degree of intentional will when sending emails, posting on social media and writing notes and marginalia, then all of that can count as meaningful daily writing.

Important paper from Google on large batch optimization. They do impressively careful experiments measuring # iterations needed to achieve target validation error at various batch sizes. The main "surprise" is the lack of surprises. [thread]

https://t.co/7QIx5CFdfJ

The paper is a good example of lots of elements of good experimental design. They validate their metric by showing lots of variants give consistent results. They tune hyperparamters separately for each condition, check that optimum isn't at the endpoints, and measure sensitivity.

They have separate experiments where the hold fixed # iterations and # epochs, which (as they explain) measure very different things. They avoid confounds, such as batch norm's artificial dependence between batch size and regularization strength.

When the experiments are done carefully enough, the results are remarkably consistent between different datasets and architectures. Qualitatively, MNIST behaves just like ImageNet.

Importantly, they don't find any evidence for a "sharp/flat optima" effect whereby better optimization leads to worse final results. They have a good discussion of experimental artifacts/confounds in past papers where such effects were reported.