Algidus

Let's say we have 3 channels in input [layer] and 64 channels/feature maps in output [layer]. How are 3 channels converted to 64 channels? How many filters do we need? To produce each / one feature map in output you will need 3 filters - one for each input channel. R - (one filter conv) - | G - (one filter conv) - | --> (summed after conv)--> | one output B - (one filter conv) - | So to generate 64 channels/feature maps in output, you'll need 3x64 filters/kernels. Thus, simply, Number of Filters = No. of Input Channels x No. of Output Channels. Or, You can think the filter is not 2D at all. Say we have an input of HxWxN, where HxW is the height and width of the image and N is the depth or number of channels, so our filter will be AxBxN, so not a 2D but an n-D filter. And we do the n-D convolution (basically a dot product of each pixel, whatever the dimension). [PS: Each filter has a bias, so AxNxN + 1 = number for params for 1 output] [Source]

Source: https://math.stackexchange.com/questions/941150/what-is-the-difference-between-independent-and-mutually-exclusive-events Summary: Mutually Exclusive: Events cannot happen at the same time. [In one experiment] Statistically Independent: Occurrence of one event doesn't affect other. [In two experiments] Mutually Exclusive: [Wikipedia] In logic and probability theory, two events (or propositions) are mutually exclusive or disjoint if they cannot both occur at the same time(be true). A clear example is the set of outcomes of a single coin toss, which can result in either heads or tails, but not both. Statistically Independent: When two events are said to be independent of each other, what this means is that the probability that one event occurs in no way affects the probability of the other event occurring. An example of two independent events is as follows; say you rolled a die and flipped a coin. The probability of getting any number face on the die in