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 1. Design a layer that takes an input and computes a tensor reduction, i.e., it returns
github: https://github.com/pandalabme/d2l/tree/main/exercises 1. What happens if you specify the input dimensions to the first layer but not to subseq
github: https://github.com/pandalabme/d2l/tree/main/exercises 1. Use the NestMLP model defined in Section 6.1 and access the parameters of the various
github: https://github.com/pandalabme/d2l/tree/main/exercises 1. What kinds of problems will occur if you change MySequential to store modules in a Py
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
