Join 📚 Josh Beckman's Highlights
A batch of the best highlights from what Josh's read, .
The way double descent is normally presented, increasing the number of model parameters can make performance worse before it gets better. But there is another even more shocking phenomenon called *data double descent*, where increasing the number of *training samples* can cause performance to get worse before it gets better. These two phenomena are essentially mirror images of each other. That’s because the explosion in test error depends on the ratio of parameters to training samples.
Double Descent in Human Learning
chris-said.io
I realised that writing isn’t about beating the competition, or being the *only* piece of work out there on a subject. Instead, an indicator of real progress within our industry — or in any aspect of human knowledge — stems from a diverse collective of people sharing their experiences of what they’ve learned, which then hopefully makes it easier for others to walk the same path in the future.
Read Widely, Apply Selectively, Share Regardless
James Stanier
Interlace is “the device of interweaving of a number of different themes … all distinct and yet inseparable.” The device is thought to have originated with Ovid. Denis Feeny in his introduction to Ovid’s Metamorphoses, notes that the “haphazard chain of association is entertaining, but it also reinforces the Ovidian theme of the very contingency of connectedness.”
Crossing the Sunshine Skyway
Robin Sloan
...catch up on these, and many more highlights
Readwise