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Saurabh Jaiswal
Saurabh Jaiswal

Saurabh Jaiswal

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From Intuitively Understanding Convolutions for Deep Learning by Irhum Shafkat

…9 = 225 parameters, with every output feature being the weighted sum of every single input feature. Convolutions allow us to do this transformation with only 9 parameters, with each output feature, instead of “looking at” every input feature, only getting to “look” at input features coming from roughly the same location. Do take note of this, as it’ll be critical to our later discussion.

From Intuitively Understanding Convolutions for Deep Learning by Irhum Shafkat

…d directly by whether it’s in the area of the kernel that produced the output or not. This means the size of the kernel directly determines how many (or few) input features get combined in the production of a new output feature.

From Common Probability Distributions by Sean Owen

n remember that product…ly distributed. Or: the exponentiation of a normally-distributed value is log-normally distributed. If sums of things are normally distributed, then remember that products of things are log-normally distributed.

Claps from Saurabh Jaiswal

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