Errata

Text: Installing Porch in Python
Issue: Possible typo, probably intended as "Torch"
Suggested fix: Change to "Installing Torch in Python"

Text: wrapped in the torchvision.models library.
Issue: missing formatting for "torchvision.models"
Suggested fix: Format as fixed-width ("code" in Atlas) analoguous to "torch.distributed" 2 paragraphs above.

Text: With Python, there are many ways to install frameworks, but the default one supported by the TensorFlow team is pip.
Issue: needs PT instead of TF
Suggested fix: replace "TensorFlow" with "PyTorch"

In multiple instances the variable "y" is written in uppercase (as a capital "Y"), while in the remainder of the chapter it is consistently in lowercase.

I might be missing something here, but I suppose this has simply been overlooked in the copy editing process.

Text: This will simply call the train function we specified five times […]
Issue: missing format for "train"
Suggested fix: format "train" as "literal" (ID and others) or "code" (Atlas)

Text: "dense layers"
Issue: very likely a tensor flow relict, as in PyTorch the term is "fully connected (fc)" as introduced in chapter 1
Suggested fix: Change wording "dense layer" → "fully connected layer". Maybe add a note explaining they are almost synonymous.

## Disclaimer: This is not meant as a serious suggestion. ###
Text: Building a CNN to Distinguish Between
Horses and Humans
Issue: Missed opportunity for a John Steinbeck reference
Suggested fix: Rename section to "Of Horses and Men"

Weekend is near… ;)

Text: Here, you’ll download some additional images for testing the model.
Issue: This contradicts the text on the previous page: ' In the preceding code snippet, you’ve already downloaded the training and validation datasets'.
Suggested fix: Not sure, what's best here. Maybe it's just a big misunderstanding on my side…… ¯\_(ツ)_/¯

Text: missing?
Issue: Expected short explanation of transforms for validation data (2nd part of code)
Suggested fix: remove # Transforms for the validation data part or add short description of what it does.

Text: If you wanted to train a dataset to recognize […]
Issue: Possible confusion of terms
Suggested fix: replace "dataset" with, e.g., "model"

Text: nn.Linear(1024, 3) # Final layer for binary classification
Issue: Comment references binary classification
Suggested fix: # Final layer for ternary classification
or similar

Text: If you explore this a little deeper, you can see that the file named scissors4.png had an output of –2.5582, –1.7362, 3.8465]
Issue: Missing opening square bracket
Suggested fix: –2.5582, –1.7362, 3.8465] → [–2.5582, –1.7362, 3.8465]

Text: While FakeData only gives image types, you could relatively easily create your own CustomData (as we looked at earlier) […]
Issue: earlier class was called "CustomDataset"
Suggested fix: change CustomData → CustomDataset

Text: One more thing to consider when using custom splits is that the name random doesn’t mean […]
Issue: name is 'random_split'
Suggested fix: replace 'random' → 'random_split'

Text: For example, batching, image augmentation, mapping to feature columns, and other such logic […]
Issue: only mention of term 'feature columns' in the book
Suggested fix: rephrase for consistency with existing material

Text: Based on the hardware you have available and the number of cores, the speed of your CPU, etc.,
Issue: context is Load (ETL), so supposedly this is rather GPU/TPU
Suggested fix: C → G

Text: This chapter covered the data ecosystem in PyTorch and introduced you to the dataset and DataLoader classes.
Issue: dataset class name should be uppercase
Suggested fix: dataset → Dataset

Text: Then, you’ll be given the sequences representing the three sentences.
Issue: only two sentences
Suggested fix: replace three → two

Text: It was a very short sentence, so it’s padded up to 85 characters with a lot of zeros!
Issue: The array contains tokens.
Suggested fix: replace "characters" → "tokens"

Text: You can then set the loss function and classifier to this. (Note that the LR is 0.001, or 1e–3.):
Issue: Text refers to optimizer, not classifier
Suggested fix: replace "classifier" → "optimizer"

Text: […] while the loss for the test set diverged after 15
epochs, the validation loss turned to increase […]
Issue: Text mentions test set while chart is labelled as "Training and Validation Loss".
Suggested fix: replace "test set" → "training set"

Text:
Figure 7-9. Accuracy for LSTM over 30 epochs
Figure 7-10. Loss with LSTM over 30 epochs
Issue: training ran für 300 epochs
Suggested fix: replace "30" → "300"

Text: This model shows a total of 406.817 parameters of which only 6,817 are trainable, so training will be fast!
Issue: period instead of decimal comma
Suggested fix: replace '406.817' → '406,817'

Text: You’ll want to create a single string with all the text and set that to be your data. Use \n for the line breaks. Then, this corpus can be easily loaded and tokenized. First, the tokenize function will split the text into individual words, and then the create_word_dictionary will create a dictionary with an index for each individual word in the text:
Issue: Missing formatting for "\n" and "tokenize".
Suggested fix: <code>\n</code>, <code>tokenize</code>.

Text: […] # features and targets […]
Issue: These were called "features and labels" before
Suggested fix: Unify to use "features and labels".

Text: As discussed in earlier chapters, with dropout, neighboring neurons are randomly dropped out (ignored) during training to avoid a familiarity bias.
Issue: Bias was introduced as "proximity bias" in ch. 3
Suggested fix: familiarity bias → proximity bias

Text: python3 -m venv chapter12env
Issue: Example is from ch 13
Suggested fix: change chapter12env → chapter13env

Text: Before running it, make sure you have a model-store (or similar) directory that you will store the archived model in.
Issue: At the beginning of the chapter this directory was created under the name "model_store".
Suggested Change model-store → model_store