New Tip 1 #241
New Tip 1 #241
Conversation
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I think this is going in a good direction. What are you thinking re: how to decide? For me it comes down to:
It might come down to more things, but this is a bit off the cuff of how I'd think about things. |
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Converting this from draft status to be ready for review. |
Benjamin-Lee
requested review from
cgreene,
rasbt,
agitter,
ajlee21 and
SiminaB
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BTW, this closes #228 |
What do you have in mind? I'm trying to get at the misleading conception that since NN are a universal function approximators they should be used universally. |
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I think the sentiment works; just needs some tweaking of the language. Maybe something like In recent years, the number of publications implementing DL in biology have risen tremendously. I still don't love the "it may appear that it is capable of anything" bit. Any suggestions? |
What about something like "a panacea for modeling problems"? |
Yes, I think that works well |
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@SiminaB thanks for the feedback. Going to bed now but will work on it tomorrow. |
content/03.when-to-use-dl.md
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| Projects such as Keras [@https://keras.io], a high-level interface for TensorFlow, make it relatively straightforward to design and test custom DL architectures. | ||
| In the future, projects such as these are likely to bring DL experimentation within reach to even more researchers. | ||
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| There are some types of problems in which using DL is strongly indicated over ML. |
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I think this part is missing "why" to synthesize the examples.
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Basically, what I'm getting at is that you don't need to start out with the most high powered tools there are now that there are more beginner-friendly ones that are stable. If you need a NN architecture that doesn't exist out of the box in one of the higher level tools but isn't so exotic that you need TensorFlow primitives, a tool like Keras might be a good middle ground.
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Of the presented options I think I prefer "Decide whether deep learning is appropriate for your problem" |
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I also like "Decide whether deep learning is appropriate for your problem". It suggests that you have a goal (problem) and want to consider appropriate tools, which may or may not be deep learning. |
Co-authored-by: Alexandra Lee <alexjlee.21@gmail.com>
[ci skip] This build is based on 43ceab7. This commit was created by the following CI build and job: https://github.com/Benjamin-Lee/deep-rules/commit/43ceab79f9126d053cf68269988ac648ecdf85f3/checks https://github.com/Benjamin-Lee/deep-rules/runs/304996701
[ci skip] This build is based on 43ceab7. This commit was created by the following CI build and job: https://github.com/Benjamin-Lee/deep-rules/commit/43ceab79f9126d053cf68269988ac648ecdf85f3/checks https://github.com/Benjamin-Lee/deep-rules/runs/304996701
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quality exposition from Daniela Witten on how regularization/penalization
and stochastic gradient descent can be viewed as two sides of the same
coin:
https://threadreaderapp.com/thread/1292293102103748609.html
same phenomenon observed with gradient boosting converging to an L1
penalized regression fit, etc.
as Hastie has said a nearly infinite number of times, if the true model is
simple, penalization will find it; if the true model is complex, only more
data will find it.
(with the caveat that truth is subjective and not necessarily important :-)
)
…--t
On Tue, Oct 13, 2020 at 2:40 PM Benjamin Lee ***@***.***> wrote:
Merged #241 <#241> into
master.
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…in-tip-2 Remove a paragraph that was moved to tip 1 in #241
Did you add yourself as a contributor if this is your first contribution?
Any more details?
Per the discussion in #228, this PR moves the content in the current tip 1 around (only removing a few lines) and creates space for a new first tip focusing on whether you should use DL in the first place.