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Shortly before his death, Alexander wrote down an OKR doc. It included:
- Conquest of Arabia and the entire Mediterranean basin
- Circumnavigation of Africa
- Development of new cities in Asia (~Middle-East) and intermixing of Eastern and Western populations
Asia was anything east of the Bosphorus.
Something to keep in mind when you'll score your own OKRs this quarter
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François Chollet
@fchollet
Tests of whether a developer-facing library API is any good:

1) You can follow a step-by-step tutorial one day, then the next day go through the same workflow from memory, without looking up any docs
2) The code written using your API by people with 1-2 weeks of experience with it (not you, not experts) looks pretty and idiomatic
3) You frequently come across implementations for use cases *you never anticipated*, that you didn't even know your API could do
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François Chollet
@fchollet
A key design principle I follow in libraries (e.g. Keras) is "progressive disclosure of complexity". Make it easy to get started, yet make it possible to handle arbitrarily flexible use cases, only requiring incremental learning at each step.

Like zooming in a complex landscape.
When you zoom in, the incremental complexity is still part of *the same picture*. Everything you're learning in the beginning will still be relevant once you've become an expert. You will be gradually diving into workflows where you're writing more and more logic from scratch.
So with something like Keras, you won't have to switch to an entire different framework as you go from student to researcher, or from data scientist to deep learning engineer.
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François Chollet
@fchollet
The number one thing to keep in mind about machine learning is that performance is evaluated on samples from one dataset, but the model is used in production on samples that may not necessarily follow the same characteristics...
The finance industry has a saying for this: "past performance is no guarantee of future results". Your model scoring X on your test dataset doesn't mean it will perform at level X on the next N situations it encounters in the real world. The future may not be like the past.
So when asking the question, "would you rather use a model that was evaluated as 90% accurate, or a human that was evaluated as 80% accurate", the answer depends on whether your data is typical per the evaluation process. Humans are adaptable, models are not.
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