satishgupta

3 ms·

1.
by satishgupta 6y ago
> Some teams build software with ML features. They may do some in-house research or reuse existing models/architectures. The ML feature can be a critical part of the product, or it can be a nice-to-have. I am more interested in this
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Ask HN: How many % of Machine Learning projects “fail”?
16 points by satishgupta 6y ago|5 comments
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Machine Learning for Developers weekly newsletter (ML4Devs, Issue 1)(ml4devs.substack.com)
29 points by satishgupta 6y ago|0 comments
4.
by satishgupta 6y ago
That is a good point. I looked at GitHub: https://madnight.github.io/githut/#/pull_requests/2020/2 And that matches with survey. How that can be reconciled? What are the biases here?
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Ask HN: Why are programming lang ranks on StackOverflow and Tiobe so divergent?
9 points by satishgupta 6y ago|8 comments
6.
by satishgupta 6y ago
First and foremost, I does not treat anything as gospel. TDD, like everythingthing else, might not make sense in all scenarios. I often use TDD for microservices. It forces me to define REST service interface from the perspective of consume
7.
by satishgupta 6y ago
I use 3-pass learning strategy: Mark, Drill, and Sweep. Mark: Quick overview of the landscape marking key ideas/sub-topics. Drill: Map task at hand to a sub-topic, drill as deep as needed to finish that task. Sweep: If needed, systemat
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by satishgupta 6y ago
... hackery of epic scale.
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by satishgupta 6y ago
Code can indeed be poetry :-)
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Scalable Efficient Big Data Pipelines Architecture(towardsdatascience.com)
1 points by satishgupta 6y ago|0 comments