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"TO DO" > "TODO

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In [commit 6072ad4](6072ad4), @KajvanRijn kindly changed all "TO DO" to "TODO" in the code blocks. That's useful. In addition, it should be changed (as here) in the instructions. Then there's no doubt or issue for anyone searching all instances.
This commit is contained in:
Mark Gotham
2025-02-11 15:11:06 +00:00
parent 666cbb02d5
commit 9649ce382b
69 changed files with 78 additions and 78 deletions
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2
Notebooks/Chap20/20_1_Random_Data.ipynb
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@@ -33,7 +33,7 @@
"\n",
"This notebook investigates training the network with random data, as illustrated in figure 20.1.\n",
"\n",
"Work through the cells below, running each cell in turn. In various places you will see the words \"TO DO\". Follow the instructions at these places and make predictions about what is going to happen or write code to complete the functions.\n",
"Work through the cells below, running each cell in turn. In various places you will see the words \"TODO\". Follow the instructions at these places and make predictions about what is going to happen or write code to complete the functions.\n",
"\n",
"Contact me at udlbookmail@gmail.com if you find any mistakes or have any suggestions.\n",
"\n"

2
Notebooks/Chap20/20_2_Full_Batch_Gradient_Descent.ipynb
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@@ -33,7 +33,7 @@
"\n",
"This notebook investigates training a network with full batch gradient descent as in figure 20.2. There is also a version (notebook takes a long time to run), but this didn't speed it up much for me. If you run out of CoLab time, you'll need to download the Python file and run locally.\n",
"\n",
"Work through the cells below, running each cell in turn. In various places you will see the words \"TO DO\". Follow the instructions at these places and make predictions about what is going to happen or write code to complete the functions.\n",
"Work through the cells below, running each cell in turn. In various places you will see the words \"TODO\". Follow the instructions at these places and make predictions about what is going to happen or write code to complete the functions.\n",
"\n",
"Contact me at udlbookmail@gmail.com if you find any mistakes or have any suggestions."
],

2
Notebooks/Chap20/20_2_Full_Batch_Gradient_Descent_GPU.ipynb
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@@ -35,7 +35,7 @@
"\n",
"This notebook investigates training a network with full batch gradient descent as in figure 20.2. This is the GPU version (notebook takes a long time to run). If you are using Colab then you need to go change the runtime type to GPU on the Runtime menu. Even then, you may run out of time. If that's the case, you'll need to download the Python file and run locally.\n",
"\n",
"Work through the cells below, running each cell in turn. In various places you will see the words \"TO DO\". Follow the instructions at these places and make predictions about what is going to happen or write code to complete the functions.\n",
"Work through the cells below, running each cell in turn. In various places you will see the words \"TODO\". Follow the instructions at these places and make predictions about what is going to happen or write code to complete the functions.\n",
"\n",
"Contact me at udlbookmail@gmail.com if you find any mistakes or have any suggestions.\n",
"\n"

2
Notebooks/Chap20/20_3_Lottery_Tickets.ipynb
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@@ -32,7 +32,7 @@
"\n",
"This notebook investigates the phenomenon of lottery tickets as discussed in section 20.2.7. This notebook is highly derivative of the MNIST-1D code hosted by Sam Greydanus at https://github.com/greydanus/mnist1d. \n",
"\n",
"Work through the cells below, running each cell in turn. In various places you will see the words \"TO DO\". Follow the instructions at these places and make predictions about what is going to happen or write code to complete the functions.\n",
"Work through the cells below, running each cell in turn. In various places you will see the words \"TODO\". Follow the instructions at these places and make predictions about what is going to happen or write code to complete the functions.\n",
"\n",
"Contact me at udlbookmail@gmail.com if you find any mistakes or have any suggestions."
]

2
Notebooks/Chap20/20_4_Adversarial_Attacks.ipynb
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@@ -33,7 +33,7 @@
"\n",
"This notebook builds uses the network for classification of MNIST from Notebook 10.5. The code is adapted from https://nextjournal.com/gkoehler/pytorch-mnist, and uses the fast gradient sign attack of [Goodfellow et al. (2015)](https://arxiv.org/abs/1412.6572). Having trained, the network, we search for adversarial examples -- inputs which look very similar to class A, but are mistakenly classified as class B. We do this by starting with a correctly classified example and perturbing it according to the gradients of the network so that the output changes.\n",
"\n",
"Work through the cells below, running each cell in turn. In various places you will see the words \"TO DO\". Follow the instructions at these places and make predictions about what is going to happen or write code to complete the functions.\n",
"Work through the cells below, running each cell in turn. In various places you will see the words \"TODO\". Follow the instructions at these places and make predictions about what is going to happen or write code to complete the functions.\n",
"\n",
"Contact me at udlbookmail@gmail.com if you find any mistakes or have any suggestions.\n"
],