github: https://github.com/pandalabme/d2l/tree/main/exercises import sys import torch.nn as nn import torch import warnings sys.path.append('/home/jov
github: https://github.com/pandalabme/d2l/tree/main/exercises import sys import torch.nn as nn import torch import warnings sys.path.append('/home/jov
github: https://github.com/pandalabme/d2l/tree/main/exercises import sys import torch.nn as nn import torch import warnings sys.path.append('/home/jov
github: https://github.com/pandalabme/d2l/tree/main/exercises import sys import torch.nn as nn import torch import warnings sys.path.append('/home/jov
github: https://github.com/pandalabme/d2l/tree/main/exercises 1. Implement average pooling through a convolution. import torch import torch.nn.functio
github: https://github.com/pandalabme/d2l/tree/main/exercises 1. Assume that we have two convolution kernels of size k_1 and k_2, respectively (with n
github: https://github.com/pandalabme/d2l/tree/main/exercises 1. Given the final code example in this section with kernel size (3,5), padding (0,1), a
github: https://github.com/pandalabme/d2l/tree/main/exercises 1. Construct an image X with diagonal edges. import torch def corr2d(X,K): h,w = K.
github: https://github.com/pandalabme/d2l/tree/main/exercises 1. Assume that the size of the convolution kernel is \Delta=0. Show that in
github: https://github.com/pandalabme/d2l/tree/main/exercises 1. Even if there is no need to deploy trained models to a different device, what are the
