Editing Talk:1838: Machine Learning

The comment that SVMs would be a better paradigm, rather than neural networks, is kind of wrong. Anyone who's worked with neural networks knows they're still essentially a linear algebra problem, just with nonlinear activation functions. Play around with tensorflow (it's fun and educational!) and you'll find most of the linear algebra isn't abstracted away as it might be in Keras, SkLearn or Caret (R). That being said, interpretability is absolutely a problem with these complex models. This is as much because the world doesn't like conforming to the nice modernist notion of a sensible theory (ie. one that can be reduced to a nice linear relationship), but even things like L1 regularisation often leave you wondering "but how does it all fit together?". On the other hand, while methods like SVMs still have a bit of machine learning magic in resolving how its hyperplane divides the hyperspace (ie. the value is derived empirically, not theoretically), the results are typically human interpretable, for a given definition of interpretable. It's no y= wx + b, but it's definitely possible. Same same for most methods short of very deep neural nets with millions of parameters. Most machine learning experts I've met have a pretty good idea what is going on in the simpler models, such as CARTs, SVMs, boosted models etc. The only reason neural nets are blackbox-y is that there's a huge amount going on inside them, and it's too much effort to do more than analyse outputs! [[Special:Contributions/172.68.141.142|172.68.141.142]] 22:43, 17 May 2017 (UTC)

The comment that SVMs would be a better paradigm, rather than neural networks, is kind of wrong. Anyone who's worked with neural networks knows they're still essentially a linear algebra problem, just with nonlinear activation functions. Play around with tensorflow (it's fun and educational!) and you'll find most of the linear algebra isn't abstracted away as it might be in Keras, SkLearn or Caret (R). That being said, interpretability is absolutely a problem with these complex models. This is as much because the world doesn't like conforming to the nice modernist notion of a sensible theory (ie. one that can be reduced to a nice linear relationship), but even things like L1 regularisation often leave you wondering "but how does it all fit together?". On the other hand, while methods like SVMs still have a bit of machine learning magic in resolving how its hyperplane divides the hyperspace (ie. the value is derived empirically, not theoretically), the results are typically human interpretable, for a given definition of interpretable. It's no y= wx + b, but it's definitely possible. Same same for most methods short of very deep neural nets with millions of parameters. Most machine learning experts I've met have a pretty good idea what is going on in the simpler models, such as CARTs, SVMs, boosted models etc. The only reason neural nets are blackbox-y is that there's a huge amount going on inside them, and it's too much effort to do more than analyse outputs! [[Special:Contributions/172.68.141.142|172.68.141.142]] 22:43, 17 May 2017 (UTC)

Does anyone else think the topic may have been influenced by Google's recently (May 17) featured article about machine learning?[[https://www.google.com/intl/en/about/main/gender-equality-films/]]

Does anyone else think the topic may have been influenced by Google's recently (May 17) featured article about machine learning?[[https://www.google.com/intl/en/about/main/gender-equality-films/]]