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Three methods and no idea how I would choose between the three of them aside randomly trying each one and measuring performance. (Not for winning kaggle but fo

by platz 8y ago

Three methods and no idea how I would choose between the three of them aside randomly trying each one and measuring performance. (Not for winning kaggle but for an actual problem)

nerdponx 8y ago
Sometimes you just need to rent 3 separate EC2 instances and try precisely that.
deleted 8y ago
[deleted]
currymj 8y ago
this is unfortunately almost always true, you just have to try every possible combination of everything with lots of hyperparameters. nothing makes any sense, it's total chaos, and we are wandering blind in the wastelands.
- platz 8y ago
  minimaxir's other comment was helpful by not trying to guess an outcome but explaining things we do know about, like training/estimation cost and order of complexity, which were topics absent from the article
platz 8y ago
"The information you'd need to choose is included in there. If you're doing this professionally, you should strive to have enough of a high-level understanding of NLP to be able to make these decisions without having a rubric handed to you on a silver platter. In a nutshell, though: Strive to use the simplest model that will get the job done. Less elaborate models are easier to understand and (usually) less prone to things like overfitting, so they'll be more tractable to work with in a business context. To that end: Use a convolutional net when you can get away with a small, fixed-size context window. Use an LSTM when you need long-term memory. Attention can be expensive, so you use it when you have cause to believe you can gain a lot by giving selective attention to features, and have both a lot of training data and a lot of computing resources. It's also worth considering that you might be best off going with none of these options. Cool as deep learning is, I've personally never actually been able to justify using it in a professional setting. Simpler models such as logistic regression and decision trees have characteristics that are near-useless for getting you to the top of a Kaggle leaderboard, but can be indispensable when working on many real-world business problems" - anonymous comment reply
- platz 8y ago
  This is the kind of context that is very helpful.

by platz 8y ago

Three methods and no idea how I would choose between the three of them aside randomly trying each one and measuring performance. (Not for winning kaggle but for an actual problem)

nerdponx 8y ago

Sometimes you just need to rent 3 separate EC2 instances and try precisely that.

deleted 8y ago

[deleted]

currymj 8y ago

this is unfortunately almost always true, you just have to try every possible combination of everything with lots of hyperparameters. nothing makes any sense, it's total chaos, and we are wandering blind in the wastelands.

platz 8y ago
minimaxir's other comment was helpful by not trying to guess an outcome but explaining things we do know about, like training/estimation cost and order of complexity, which were topics absent from the article

platz 8y ago

"The information you'd need to choose is included in there. If you're doing this professionally, you should strive to have enough of a high-level understanding of NLP to be able to make these decisions without having a rubric handed to you on a silver platter. In a nutshell, though: Strive to use the simplest model that will get the job done. Less elaborate models are easier to understand and (usually) less prone to things like overfitting, so they'll be more tractable to work with in a business context. To that end: Use a convolutional net when you can get away with a small, fixed-size context window. Use an LSTM when you need long-term memory. Attention can be expensive, so you use it when you have cause to believe you can gain a lot by giving selective attention to features, and have both a lot of training data and a lot of computing resources. It's also worth considering that you might be best off going with none of these options. Cool as deep learning is, I've personally never actually been able to justify using it in a professional setting. Simpler models such as logistic regression and decision trees have characteristics that are near-useless for getting you to the top of a Kaggle leaderboard, but can be indispensable when working on many real-world business problems" - anonymous comment reply

platz 8y ago
This is the kind of context that is very helpful.