a.) Let's consider first 5 layers of CNN AlexNet and calculate: size and number of activation maps in each layer as well as number of weights (only in 5 layers) that needs to be optimized using back-propagation algorithm. b.) Which type on machine learning is used by CNNs (supervised or unsupervised learning) Case Study: AlexNet [Krizhevsky et al. 2012] Full (simplified) AlexNet architecture: [227x227x3] INPUT Stride Max pooling 3224 128 Max pooling 282 128 dense 128 Max pooling CONV1: 96 11x11 filters at stride 4, pad 0 MAX POOL1: 3x3 filters at stride 2 NORM1: Normalization layer CONV2: 256 5x5 filters at stride 1, pad 2 MAX POOL2: 3x3 filters at stride 2 (only relevant part of AlexNet is listed above however the figure shows full AlexNet) fo dense

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