All Questions
3 questions
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Deep Learning: Architecture vs. Features for Performance?
In deep learning, when aiming for peak metric performance, is a well-designed architecture with imperfect features/dataset generally preferable to a poorly designed architecture with high-quality ...
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Why disentangling the features of variation in representation?
Consider the following excerpt from abstract of the research paper titled Better Mixing via Deep Representations by Yoshua Bengio et al.
It has been hypothesized, ...
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When working with time-series data, is it wrong to use different time-steps for the features and target?
When working with time-series data, is it wrong to use daily prices as features and the price after 3 days as the target?
Or should I use the next-day price as a target, and, after training, predict 3 ...
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