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v2.1 of Ch10

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Jonathan Taylor
2023-08-20 19:58:59 -07:00
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Ch10-deeplearning-lab.Rmd
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---
jupyter:
jupytext:
cell_metadata_filter: -all
formats: ipynb,Rmd
text_representation:
extension: .Rmd
format_name: rmarkdown
format_version: '1.2'
jupytext_version: 1.14.7
kernelspec:
display_name: Python 3 (ipykernel)
language: python
name: python3
---
# Chapter 10 # Chapter 10
@@ -872,7 +856,7 @@ for idx, (X_ ,Y_) in enumerate(cifar_dm.train_dataloader()):
Before we start, we look at some of the training images; similar code produced Before we start, we look at some of the training images; similar code produced
Figure 10.5 on page 164. The example below also illustrates Figure 10.5 on page 447. The example below also illustrates
that `TensorDataset` objects can be indexed with integers --- we are choosing that `TensorDataset` objects can be indexed with integers --- we are choosing
random images from the training data by indexing `cifar_train`. In order to display correctly, random images from the training data by indexing `cifar_train`. In order to display correctly,
we must reorder the dimensions by a call to `np.transpose()`. we must reorder the dimensions by a call to `np.transpose()`.
@@ -1706,7 +1690,6 @@ early stopping, since then the test performance would be biased.
We form the training dataset similar to We form the training dataset similar to
our `Hitters` example. our `Hitters` example.
```{python} ```{python}
datasets = [] datasets = []
for mask in [train, ~train]: for mask in [train, ~train]:

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Ch10-deeplearning-lab.ipynb
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