Convolution layer (CONV) The convolution layer (CONV) takes advantage of filters that perform convolution functions as it really is scanning the input $I$ with regard to its dimensions. Its hyperparameters include things like the filter size $F$ and stride $S$. The resulting output $O$ is called feature map or https://financefeeds.com/copyright-analysts-makes-cycle-top-predictions-for-copyright-coin-bnb-and-ripple-xrp-prices-in-2025/