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Comment by wslh | Hacker News Reader

Genuine question: how do you benefit from this classes if you just want to use third-party libraries? I mean, you can now do amazing things just using others' APIs without knowing what's behind.

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If you want to use a library to find the shortest path in a graph, then the input is simply the list of nodes and edges and is easy to understand. You can use a third party library to solve the problem without knowing how the algorithm works under the hood.

But the arguments to a ML algorithm provided by third-party libraries include various parameters to tune the algorithm and make tradeoffs between accuracy/time etc. Debugging the algorithm for your particular dataset also requires some knowledge of how the algorithm works.

But there are use cases where you need not know what's happening behind the scenes. For example, you can use a ML as a Service provider to classify your photos without knowing how their algorithms work.

There seems to be a bit of an art to choosing your machine learning algorithm and parameters. The class is a good way to get a grasp on that.

I agree. Also, I think there are going to be a proliferation of API's to choose from and it will be helpful to understand which ones are the best and why. Some could be flat out wrong.

You will get more accurate results that are better motivated if you understand the underlying algorithm. Just like you get better results if you understand your data.

Don't rely on a magic box.

> Don't rely on a magic box.

That's quite funny given the circumstances. I think the neural network algorithm disagrees, and it really wants you to rely on a magic box.

Have you tried training deep learning models before? Deep learning requires a ton of hyperparameter and architectural tuning, and there are a bajillion data transformation, mathematical optimization, etc. tricks that people use. The end product is still a magic box, but there's a ton of background knowledge required to train these models well.

This set of lectures provides a nice balance between providing a (relatively) gentle theoretical basis for the algorithms and also providing code labs in R for using R library implementations.

http://www.r-bloggers.com/in-depth-introduction-to-machine-l...

Somebody has to be the 'others'. Somebody has to know what's behind.

you still need to know what is appropriate to try. Depending on the problem there might be colossal mistakes that you can make just because you don't know what's behind the scenes.

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