Convolution layer (CONV) The convolution layer (CONV) takes advantage of filters that perform convolution functions as it can be scanning the input $I$ with respect to its Proportions. Its hyperparameters consist of the filter size $F$ and stride $S$. The ensuing output $O$ is called aspect map or activation https://financefeeds.com/2025s-best-ico-cryptos-for-potential-explosive-returns-could-they-be-the-key-to-the-next-bull-run/