scarecrow112

> just a fluke in brain that gave her a prototype math co-processor.

Would a better word of choice be "predisposition to do arithmetic"? I've been wondering the same with artists in general (painters, musicians, etc.). Would a person without the predisposition be able to practice say Piano/Vocals for years together and reach a certain level of artistry? Any reference to academic studies on this would be appreciated.

EDIT: Formatting

This is interesting and could be a savior for Machine Learning(ML) engineering teams. In a typical ML workflow, there are three main entities to be managed: 1. Code 2. Data 3. Models Systems like Data Version Control(DVC) [1], are useful for versioning 2 & 3. DVC improves on usability by residing inside the project's main git repo while maintaining versions of the data/models that reside on a remote. With Git partial clone, it seems like the gap could still be reduced between 1 & 2/3.

[1] - https://dvc.org/

The post lists Auto Differentiation as one of the techniques that can overcome non-differentiable loss functions. Can someone explain how this is even possible? After all automatic differentiation[1] is a way to compute gradients/derivatives in a way which could become costly if otherwise done(symbolic differentiation[2]). The function or the operations defined in the function(in case of source-to-source differentiation[3]) needs to be differentiable.

[1] - https://www.youtube.com/watch?v=z8GyNneq5D4

[2] - https://stackoverflow.com/a/45134000

[3] - https://github.com/google/tangent

EDIT: 1. Added reference. 2. Formatting

Jeez this is scary! This very much sounds like the domain blocking incident [1] that Zoho faced sometime back.

[1] - https://news.ycombinator.com/item?id=18059792