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 $File$ and stride $S$. The resulting output $O$ is called attribute map or https://financefeeds.com/heres-why-blockdag-link-near-dot-are-the-best-cryptocurrencies-for-high-returns-in-2025/