alextp

When claire stopped writing the tgif emails I was very sad, among others I talked to. When I heard, years later, about all this bs she went through, it helped crack the illusion for me. So sad.

The flip side is redistributive pensions require an ever growing population and most European pension systems will go bankrupt within a couple of decades given current birth and immigration rates.

Choose the top N according to the proxy objective and then use the real objective to choose the best out of those N candidates.

Claire deWitt and the city of the dead, by Sara Gran is a good example. Very colorful language.

Also the first volumes of the Berlin Noir "trilogy".

How is brain separate from the rest of google?

The historic picture makes a little more sense (though this is not something a 5yo would understand).

We call these things embeddings because you start with a very high dimensional space (image a space with one dimension per word type, where each word is a unit vector in the appropriate dimension) and then approximate distances between sentences / documents / n-grams in this space using a space with much smaller dimensionality. So we "embed" the high dimensional space in a manifold in the lower dimensional space.

It turns out though that these low dimensional representations satisfy all sorts of properties that we like which is why embeddings are so popular.

I've contributed to autograph and would love to answer any questions.

Cool! Did you try using tensorflow's eager execution?

I think Keras is a real deal framework. It provides a higher-level API than most other frameworks, but it has pretty sweet portability of models across frameworks and platforms and most research papers are implementable in Keras without too much trouble.

We're still fairly early in the project, so for now threading is the only supported way.

We can do better, however, and we're working on ways to leverage the hardware better (for example, if you have no data-dependent choices in your model we can enqueue kernels in parallel on all GPUs in your machine at once from a single python thread, which will perform much better than explicit python multithreading).

Stay on the lookout as we release new experimental APIs to leverage multiple GPUs and multiple machines.

Ah, I didn't know SavedModel didn't work in android. I think freezing is still the way to go there? I'm sorry, I don't personally work on the mobile side of things.

Did you try using SavedModel? It should be seamless to use downstream with tensorflow serving and it's not that hard to get estimators to spit those out.

I write tests not to convince myself my code is correct (it often is, and repl and ad-hoc testing are more than enough to make sure it does the right thing now) but to prevent myself & future others from breaking it as they maintain it in the future.