amin
  • Member for 1 year, 3 months
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Smallest possible network to approximate the $sin$ function
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6 votes

Before anything, the function you have wrote for the network lacks the bias variables (I'm sure you used bias to get those beautiful images, otherwise your tanh network had to start from zero). ...

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Can you use machine learning for data with binary outcomes?
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2 votes

Of course, you can use AI (especially Deep Learning) in your application. Your covariates will be the input to your AI model and the model should predict the probability of presence. The model has no ...

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How does vanish gradient restrict RNN to not work for long range dependencies?
2 votes

Vanishing gradient is: as the gradient starts to flow from the end of the network (right side of the network) to the start of the network (left side of the network), it will be multiplied by numbers ...

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Appropriate convolutional neural network architecture when the input consists of two distinct signals
1 votes

I don't know what you mean by desctized signals but if I understand your question correctly, separating two signal and passing them through same architecture of CNN (even with different parameters) is ...

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Which NN would you choose to estimate a continuous function $f:\mathbb R^2 \rightarrow \mathbb R$?
1 votes

It depends on the complexity of your problem. $\mathbb{R}^2 \rightarrow \mathbb{R}^1$ looks simple, but I can give you some nonsense complicated examples that need a deep network. So, the complexity ...

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Why does the accuracy drop while the loss decrease, as the number of epochs increases?
1 votes

Decrease of loss does not essentially lead to increase of accuracy (most of the time it happens but sometime it may not happen). To know why, you can have a look at this question. The network cares ...

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Should I use additional empty category in some categorical problems?
1 votes

I know this is not a straight answer to your question, but I couldn't comment on your post so decided to post it (so maybe I will delete it after you received a better answer). I think this playlist ...

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How does back-propagation through time work for optimizing the weights of a bidirectional RNN?
1 votes

I have not implement the backprop of a bi-directional RNN from scratch so I can't be sure my answer is correct but I hope it helps. You can see how bi-directional RNN works from this video from Andrew ...

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In CNNs, why do we sum the filter derivatives w.r.t the loss function to get the final gradient?
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0 votes

I really liked the question. Yes, we sum over derivatives. First of all think what backpropagation is trying to do: finding the affect of each parameter on the loss. So as you said: the same filter ...

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