Intro to Statistical Learning (2nd Ed), Solution to Problem 10.4c

10.4C: Consider a CNN that takes in 32 × 32 grayscale images and has a single convolution layer with three 5 × 5 convolution filters (without boundary padding) (a) Draw a sketch of the input and first hidden layer similar to Figure 10.8. (b) How many parameters are in this model. (c) Explain how this model can be thought of as an ordinary feedforward neural network with the individual pixels as inputs, and with constraints on the weights in the hidden units. What are the constraints? (d) If there were no constraints, then how many weights would there be in the ordinary feed-forward neural network in (c)? Download Book: https://www.statlearning.com/ Authors' Lectures (R): https://youtube.com/playlist?list=PLoROMvodv4rOzrYsAxzQyHb8n_RWNuS1e&si=NP0wJ6RjP8XkxU7y Authors' Lectures (Python): https://youtube.com/playlist?list=PLoROMvodv4rPP6braWoRt5UCXYZ71GZIQ&si=0Z8tx4xlPLEyjZ70 https://colab.research.google.com/drive/16rgV6bj457Vlb7OgQsLPWT8p7REFVKUM?usp=sharing