
Ask HN: What deep learning papers should I implement to learn? - pandeykartikey
I have been wanting to implement a Deep Learning Paper to get some hands on the current state of the art model or current field of research. But, generally the paper I pickup is a bit tough to understand. So, I was looking if anyone could suggest me a paper which would be some latest research but slightly easier to grasp?
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malux85
Pushing the barriers or independent validation on the latest research is not a
good place to start if you're new to the area.

Go and implement an image classifier, tweak the parameters and the topology
and see how the results and training changes.

Switch to a time series model / text model, and learn the difference between
convolution / recurrent networks.

Start playing with non-sequential topologies, custom objective functions,
Q-Learning,

Once you have a grasp of these basics go back and read the papers, and you'll
find that you understand them a lot more, and you'll see where they're pushing
the boundaries.

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ck_one
If he wants to focus on Deep Learning I wouldn't touch Reinforcement Learning
(you mentioned Q-learning). Lots of hours can be wasted on RL.

~~~
malux85
Agreed!

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ck_one
That's actually a pretty good idea to improve your skills. I would highly
recommend to implement one of the semantic segmentation papers. Perhaps start
with a simple FCN (with and without a pretrained encoder).

