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. 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
github: https://github.com/pandalabme/d2l/tree/main/exercises import torch import warnings import matplotlib.pyplot as plt import sys sys.path.append(
github: https://github.com/pandalabme/d2l/tree/main/exercises 1. Denote by L_v the validation loss, and let
github: https://github.com/pandalabme/d2l/tree/main/exercises 1. Does reducing the batch_size (for instance, to 1) affect the reading performance? Red
github: https://github.com/pandalabme/d2l/tree/main/exercises 1. We can explore the connection between exponential families and softmax in some more d
github: https://github.com/pandalabme/d2l/tree/main/exercises 1. Experiment with the value of \lambda in the estimation problem in this section. Plot
github: https://github.com/pandalabme/d2l/tree/main/exercises 1. When can you solve the problem of polynomial regression exactly? Polynomial regressio
