Chapter 8: Convolutional Neural Networks (CNNs) in PyTorch
Abstract : Convolutional Neural Networks (CNNs) in PyTorch are a fundamental architecture for image processing and computer vision tasks. PyTorch provides robust tools within its torch.nn module to easily define, build, and train CNNs. Key Components of a CNN in PyTorch: Convolutional Layers ( nn.Conv2d ): These layers apply a set of learnable filters (kernels) to the input image, extracting features such as edges, textures, or more complex patterns. Key parameters include in_channels , out_channels , kernel_size , stride , and padding . Activation Functions: Non-linear activation functions, commonly ReLU ( nn.ReLU ), are applied after convolutional layers to introduce non-linearity, enabling the network to learn more complex relationships. Pooling Layers ( nn.MaxPool2d , nn.AvgPool2d ): These layers reduce the spatial dimensions (width and height) of the feature maps, thereby reducing the number of...