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. Suppose that we design a deep architecture to represent a sequence by stacking self-a
github: https://github.com/pandalabme/d2l/tree/main/exercises 1. Visualize attention weights of multiple heads in this experiment. import sys import t
github: https://github.com/pandalabme/d2l/tree/main/exercises 1. Replace GRU with LSTM in the experiment. import sys import torch.nn as nn import torc
github: https://github.com/pandalabme/d2l/tree/main/exercises 1. Implement distance-based attention by modifying the DotProductAttention code. Note th
github: https://github.com/pandalabme/d2l/tree/main/exercises 1. Parzen windows density estimates are given by \hat{p}(x)=\frac{1}{n}\sum_ik(x,x_i)
github: https://github.com/pandalabme/d2l/tree/main/exercises import sys import torch.nn as nn import torch import warnings from sklearn.model_selecti
github: https://github.com/pandalabme/d2l/tree/main/exercises import sys import torch.nn as nn import torch import warnings import numpy as np from sk
github: https://github.com/pandalabme/d2l/tree/main/exercises 1. Can we treat exhaustive search as a special type of beam search? Why or why not? We c
github: https://github.com/pandalabme/d2l/tree/main/exercises 1. Suppose that we use neural networks to implement the encoder–decoder architecture. Do
github: https://github.com/pandalabme/d2l/tree/main/exercises 1. Try different values of the max_examples argument in the _tokenize method. How does t
