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Claiming that gradient descent is a great algorithm certainly wasn't the goal of this article. But GD is the basis from which most other iterative algorithms st

by eliben 10y ago

Claiming that gradient descent is a great algorithm certainly wasn't the goal of this article. But GD is the basis from which most other iterative algorithms stem, and it's worthwhile having a good understanding of its inner workings as a prerequisite for more advanced algorithms. Personally I found http://sebastianruder.com/optimizing-gradient-descent/ http://sebastianruder.com/optimizing-gradient-descent/ interesting - it goes into the advanced variants like nesterov, adadelta, etc

jordigh 10y ago
I realise, but since a lot more gets written about steepest descent than everything else, it also gets implemented and used a lot more often, even when other alternatives are readily available and already implemented. For example, the trainscg method of Matlab is relatively obscure thus unused and not frequently re-implemented. There's an implicit endorsement in these blog posts. People wouldn't be spilling all this ink nowadays on plain adalines, even if they're building blocks for backprop networks, right? So by writing so much about it, people get the impression that it has to be studied and implemented very carefully, to the exclusion of better methods.
- maxerize 10y ago
  As the above poster said, if you can introduce people to a topic by explaining a simplified or 'naive' solution/algorithm, then that could be a good springboard to learn more about the topic. Which is why there should be more comments presenting improvements and alternatives, rather than criticising what is obviously meant as a primer on optimisation. From the intro: Gradient descent is a standard tool for optimizing complex functions iteratively within a computer program. Its goal is: given some arbitrary function, find a minima. For some small subset of functions - those that are convex - there's just a single minima which also happens to be global. For most realistic functions, there may be many minima, so most minima are local. Making sure the optimization finds the "best" minima and doesn't get stuck in sub-optimial minima is out of the scope of this article. Here we'll just be dealing with the core gradient descent algorithm for finding some minima from a given starting point.
  - theoh 10y ago
    The way the author uses the word "minima" as if it was both singular and plural is a really discouraging sign.

by eliben 10y ago

jordigh 10y ago

I realise, but since a lot more gets written about steepest descent than everything else, it also gets implemented and used a lot more often, even when other alternatives are readily available and already implemented. For example, the trainscg method of Matlab is relatively obscure thus unused and not frequently re-implemented. There's an implicit endorsement in these blog posts. People wouldn't be spilling all this ink nowadays on plain adalines, even if they're building blocks for backprop networks, right? So by writing so much about it, people get the impression that it has to be studied and implemented very carefully, to the exclusion of better methods.

maxerize 10y ago
As the above poster said, if you can introduce people to a topic by explaining a simplified or 'naive' solution/algorithm, then that could be a good springboard to learn more about the topic. Which is why there should be more comments presenting improvements and alternatives, rather than criticising what is obviously meant as a primer on optimisation. From the intro: Gradient descent is a standard tool for optimizing complex functions iteratively within a computer program. Its goal is: given some arbitrary function, find a minima. For some small subset of functions - those that are convex - there's just a single minima which also happens to be global. For most realistic functions, there may be many minima, so most minima are local. Making sure the optimization finds the "best" minima and doesn't get stuck in sub-optimial minima is out of the scope of this article. Here we'll just be dealing with the core gradient descent algorithm for finding some minima from a given starting point.
- theoh 10y ago
  The way the author uses the word "minima" as if it was both singular and plural is a really discouraging sign.