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There's more than one way to do it: find the learning that's right for you (and your data)
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#29
opened Nov 11, 2018 by
chevrm
1 task done
Use existing model architectures whenever possible.
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#6
opened Oct 24, 2018 by
Benjamin-Lee
If you don't have enough data, consider whether there is other data that can help pre-train a model.
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#8
opened Nov 1, 2018 by
cgreene
Strive for balance between training, tuning, and testing datasets
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#19
opened Nov 7, 2018 by
hugoaerts
For full reproducibility, always report all details of your analyses, including sharing of data, methods, and models
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#21
opened Nov 7, 2018 by
hugoaerts
Make sure your problem is a good fit for deep learning.
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#22
opened Nov 10, 2018 by
beamandrew
Understand the trade-off between interpretability and performance
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#25
opened Nov 11, 2018 by
signalbash
If a human can't perform the task with ease, be surprised if a machine can.
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#26
opened Nov 11, 2018 by
paulbrodersen
1 task done
Fairly validate your model with proper test/train data
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#27
opened Nov 11, 2018 by
chevrm
1 task done
Don't use deep learning if you don't have to.
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#3
opened Oct 24, 2018 by
Benjamin-Lee
Understand, protect against, and benchmark possible overfitting
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#28
opened Nov 11, 2018 by
chevrm
1 task done
So you have a model/classifier....now what
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#30
opened Nov 11, 2018 by
chevrm
1 task done
Treat it as a statistical model
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#33
opened Nov 12, 2018 by
blengerich
1 task done
Your deep learning model does not need to be a black box.
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#36
opened Nov 13, 2018 by
tbrittoborges
1 task done
Don't forget your ML fundamentals/Rules that apply to ML also apply to DL
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#37
opened Nov 14, 2018 by
Benjamin-Lee
1 task done
Make sure you are optimizing the right objective function!
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#38
opened Nov 14, 2018 by
Isy89
1 task done
Use a simple linear and non-linear model as a baseline for measuring progress.
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#41
opened Nov 14, 2018 by
rasbt
1 task done
Perform sanity checks and follow good coding practices
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#52
opened Nov 21, 2018 by
evancofer
2 tasks done
Be suspicious: high prediction accuracy may backfire
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#55
opened Nov 21, 2018 by
HaohanWang
2 tasks done
A primer on deep learning in genomics
paper
Papers we should cite
#59
opened Nov 27, 2018 by
agitter
mlf-core: a framework for deterministic machine learning
paper
Papers we should cite
#361
opened May 14, 2021 by
agitter
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