1. Line regression 1.1 Ordinary Least Squares (OLS) Perspective 1.1.1 Model Representation: The linear regression model is represented as: \hat{Y} = X
github: https://github.com/pandalabme/d2l/tree/main/exercises import pandas as pd import time from tqdm import tqdm import sys import torch import tor
github: https://github.com/pandalabme/d2l/tree/main/exercises import time from tqdm import tqdm import sys import torch import torchvision from torchv
github: https://github.com/pandalabme/d2l/tree/main/exercises 1. In what sense do traditional complexity-based measures fail to account for generaliza
github: https://github.com/pandalabme/d2l/tree/main/exercises 1. Can you design other cases where a neural network might exhibit symmetry that needs b
github: https://github.com/pandalabme/d2l/tree/main/exercises 1. Assume that the inputs X to some scalar function f are n*m matrices. What is the dime
github: https://github.com/pandalabme/d2l/tree/main/exercises 1. Change the number of hidden units num_hiddens and plot how its number affects the acc
github: https://github.com/pandalabme/d2l/tree/main/exercises 1. Show that adding layers to a linear deep network, i.e., a network without nonlinearit
github: https://github.com/pandalabme/d2l/tree/main/exercises 1. What could happen when we change the behavior of a search engine? What might the user
github: https://github.com/pandalabme/d2l/tree/main/exercises 1. If we wish to estimate the error of a fixed model f to within 0.0001 with probability
github: https://github.com/pandalabme/d2l/tree/main/exercises
