Errata

9 numerical attributes, not 11

"You can see that the regularization forced the model to have a smaller slope"
should be "bigger slope" for this data according to the figure.

The build() method is missing the super().build(batch_input_shape) call that is referenced in the code walk through latter in the same page.

After Equation 4-16 it is stated „This cost function makes sense because -log(t) grows very large when t approaches 0, so the cost will be large if the model estimates a probability close to 0 [..]“

As I understand it, there should be „-log(p)“ instead of „-log(t)“ in this paragraph and the following sentences. In fact, the probability (i.e. sigma(t) ) gets zero only for negative values of t, whereas log(t) is not defined for negative t. This contradicts the quoted statement above.

In the next to last sentence (beginning "Notice that Jupyter notebooks use Markdown..."), the example markdown for linked text is reversed. The link text is placed within the square brackets, while the URL is placed within the parentheses.

Trying to run the whole code written in pycharm, the following yields an error:
corr_matrix = housing.corr() because of the "ocean_proximity" parameter beeing a string.

To ensure things work better, a parameter can be added to the corr() method to force it only to use numeric values as follows:
corr_matrix = housing.corr(numeric_only = True)

I think the U and V matrices of the SVD are mixed up.
the coefficients are in the V matrix

but the unit vectors of the principal components are in the U matrix of the SVD.

y_representative_digits is written correctly in the books but in the Github as well as the google colab notebooks its written wrong. the y_representative_digits values are incorrect and this leads to an extremely low accuracy (10%). the erroneous line is:
y_representative_digits = np.array([
0, 1, 3, 2, 7, 6, 4, 6, 9, 5,
1, 2, 9, 5, 2, 7, 8, 1, 8, 6,
3, 2, 5, 4, 5, 4, 0, 3, 2, 6,
1, 7, 7, 9, 1, 8, 6, 5, 4, 8,
5, 3, 3, 6, 7, 9, 7, 8, 4, 9])

The first sentence ends without a space:
"... add_loss() method.For example..."

In this bullet point, you said that YOLOv3 outputs 45 numbers per grid cell - 5 bounding boxes times 4 coordinates plus 5 objectness scores plus 20 class probabilities, but this is true only for YOLOv1 where each grid cell was able to find only one object - which is pretty straightforward because we have only one set of class probabilities. Since YOLOv2 the class probabilities are decoupled from a single grid cell, here is the quote from YOLOv2 paper which is also valid for YOLOv3 because nothing changed in this topic:
"When we move to anchor boxes we also decouple the class prediction mechanism from the spatial location and instead predict class and objectness for every anchor box"

So according to this sentence if you have 20 classes and want to find 5 boxes for each grid cell you will have 125 numbers per grid cell 5 boxes x (4 [box size and localization] + 1 objectness score + 20 class probabilities)

"Faster-RCNN", should be "Faster R-CNN"

This is useful if you want do not want Tensorflow... -> This is useful if you do not want Tensorflow...

The sentence starts with: "It best to assume..."
Should be: "It is best to assume..."