Row 8077

Row ID: 8077 | Dataset Entry | Axioma AXP Content Repository

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As for any other field, there’s only one way: read. Read cornerstone papers to get the concepts, then read derived papers to understand more general ideas. Finally, ask yourself questions and try to answer those.

An example: I read the residual network paper (Ho et al. 2016 IIRC). Then, I check relevant related papers that include RNNs. By reading, you eventually acquire the idea that the recurrence extends the temporal horizon of your representation, that is, by using a recurrent network your model can remember things from the past (very very simplified explanation here).

Finally, you need to decide a model for your own project. You ask yourself “does my model need to remember things from the past?” If the answer is yes, then you know that you have to use a recurrent network. Else, you don’t. Supposing that you need a RNN, the knowledge acquired by now should be enough to let you at least try an implementation from scratch. If it doesn’t, go back to the papers you already read. Eventually you’ll find limitations of your model, and you will extend it by repeating the process.

FieldValue
text As for any other field, there’s only one way: read. Read cornerstone papers to get the concepts, then read derived papers to understand more general ideas. Finally, ask yourself questions and try to answer those. An example: I read the residual network paper (Ho et al. 2016 IIRC). Then, I check relevant related papers that include RNNs. By reading, you eventually acquire the idea that the recurrence extends the temporal horizon of your representation, that is, by using a recurrent network your …
label r/deeplearning
dataType comment
communityName r/deeplearning
datetime 2024-05-18
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Raw Record

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  "text": "As for any other field, there’s only one way: read. Read cornerstone papers to get the concepts, then read derived papers to understand more general ideas. Finally, ask yourself questions and try to answer those.\n\nAn example: I read the residual network paper (Ho et al. 2016 IIRC). Then, I check relevant related papers that include RNNs. By reading, you eventually acquire the idea that the recurrence extends the temporal horizon of your representation, that is, by using a recurrent network your model can remember things from the past (very very simplified explanation here). \n\nFinally, you need to decide a model for your own project. You ask yourself “does my model need to remember things from the past?” If the answer is yes, then you know that you have to use a recurrent network. Else, you don’t. Supposing that you need a RNN, the knowledge acquired by now should be enough to let you at least try an implementation from scratch. If it doesn’t, go back to the papers you already read. Eventually you’ll find limitations of your model, and you will extend it by repeating the process.",
  "label": "r/deeplearning",
  "dataType": "comment",
  "communityName": "r/deeplearning",
  "datetime": "2024-05-18",
  "username_encoded": "Z0FBQUFBQm5LakwzN2VrOGFvWTl6Zktfazlvb2VQMkUyTEEtSkdKcFk4WjlUdWlTS1lvWWdDV1NodVVEa2tfY1NPSktvRjhQNGdTTmludmNnNW9vYktfOTI4R1F3Z01aMmc9PQ==",
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