The errata list is a list of errors and their corrections that were found after the product was released.
The following errata were submitted by our customers and have not yet been approved or disproved by the author or editor. They solely represent the opinion of the customer.
| Version |
Location |
Description |
Submitted by |
Date submitted |
|
Chapter 23, Section 6
Last Paragraph |
there is minor typo here: "while the Python library takes car ("takes care") of all the server ".
|
Mehran |
Jul 02, 2023 |
| Printed |
Page p.256
in the middle |
'We will discuss handling imbalanced classes during preprocessing in Recipe 17.5.'
This paragraph should refer Recipe 5.5 instead of 17.5.
|
HIDEMOTO NAKADA |
Apr 22, 2024 |
| Printed |
Page p.274
comment in the code |
"# Search the index for the two nearest neighbors"
"two" should be "ten".
|
HIDEMOTO NAKADA |
Apr 22, 2024 |
| Printed |
Page p.278, p.281
comment in the code |
"# Create decision tree regression object"
The comment seems to be wrong, since we are not using decision tree regression here.
|
HIDEMOTO NAKADA |
Apr 22, 2024 |
| Printed |
Page p.301
in the end |
array([[0.31859969, 0.63663466, 0.04476565]])
the above line repeats twice
the first occurence should be
lassifer_sigmoid.predict_proba(new_observation)
|
HIDEMOTO NAKADA |
Apr 22, 2024 |
| Printed |
Page p.375
comment in the code |
"# Save the model to a directory called `save_model`"
`save_model` should be `saved_model`
|
HIDEMOTO NAKADA |
Apr 22, 2024 |
| PDF |
Page p.328
the code |
The code creates standard scalar but does not use.
we need to have something like
features = scaler.fit_transform(features)
|
HIDEMOTO NAKADA |
Apr 25, 2024 |
| PDF |
Page p.5
in the middle of the code |
# Generate a matrix of shape (3,3) containing all ones
matrix = np.full(shape=(3,3), fill_value=1)
the fill_value should be '1.0' instead of '1', since the following output shows that the datatype is floating, not int.
Instead, you could fix the output to int. They have to be consistent.
|
HIDEMOTO NAKADA |
May 20, 2024 |
| PDF |
Page p.5
in the middle of the code |
# Generate a matrix of shape (3,3) containing all ones
matrix = np.full(shape=(3,3), fill_value=1)
the fill_value should be '1.0' instead of '1', since the following output shows that the datatype is floating, not int.
Instead, you could fix the output to int. They have to be consistent.
|
HIDEMOTO NAKADA |
May 20, 2024 |
| PDF |
Page p.95
in the middle. |
# Get feature names
feature_names = dictvectorizer.get_feature_names()
get_feature_names is renamed to get_feature_names_out
|
HIDEMOTO NAKADA |
May 20, 2024 |
| PDF, ePub |
Page p.114
in the middle |
`We can use the get_feature_names method to view the word associated with each feature:`
'get_feature_names' should be 'get_feature_names_out'
|
HIDEMOTO NAKADA |
May 20, 2024 |
| Printed |
Page 1.0 Introduction
Page 1 Creating a Vector |
I dont believe anything is wrong with the text, my problem is with setting up my Python environment within Visual Studio to facilitate navigating through the examples within the text.
after using GitHub's Cookiecutter extension instructions for the VS setup for Python, a simple library load command "import numpy as np" gave me the following error "An error was encountered while opening associated documents the last time this solution was loaded. Document load is being skipped during this solution load in order to avoid that error."
Python 3.9 (64-bit) interactive window [PTVS 17.0.23189.3-17.0]
Type $help for a list of commands.
>>> 4+4
8
>>> #Load library
... import numpy as np
Traceback (most recent call last):
File "<stdin>", line 2, in <module>
ModuleNotFoundError: No module named 'numpy'
>>>
|
C J Houston |
Oct 16, 2023 |
| Other Digital Version |
1.19 Inverting a Matrix
Code snippets |
# Multiply matrix and its inverse
matrix @ np.linalg.inv(matrix)
array([[ 1., 0.],
[ 0., 1.]])
Need to call np.round(matrix @ np.linalg.inv(matrix)) to get the desired output.
|
Vimal Kaur |
Apr 11, 2023 |
| Other Digital Version |
3.3 Slicing DataFrames
Code snippets |
# Select four rows
dataframe.iloc[1:4]
This selects 3 rows instead of 4 so comment needs to be updated
# Select three rows
dataframe.iloc[:4]
This select 4 rows instead of 3 as comment needs to be updated.
|
Vimal Kaur |
Apr 11, 2023 |
| Other Digital Version |
4.4
Solution, explaination of degree=3 |
Polynomial features given as:
x1 , x2 , x1^2 , x2^2 , x1^3 , x2^3 , x1^ 2 , x1^3 , x2^3
which should be:
x1, x2, x1^2, x1*x2, x2^2, x1^3, x1^2*x2, x1*x2^2, x2^3
|
Dhani Prasad |
Aug 07, 2023 |
| Printed |
Page 26
Section 2.3 |
The sample data files from the first edition that were hosted on Chris Albon's github repo here (used starting in Chapter 2 and elsewhere in the book) are no longer available.
I'm not sure if these are available directly or there are new links in the Dockerized repo posted by Kyle Gallatin - I was able to successfully mount and run it but Jupyter comes up empty - for us Windows users Docker can be hit-or-miss, and I gave up troubleshooting it. I also think a lot of novice / intermediate Python users (especially students non-Linux readers) will be unfamiliar with it, so a simple location for the sample data would be helpful.
If a new repo is available and links could be posted on the book's resources page that would be awesome, or possibly just zip all the sample files for download and post on the Docker repo - I think it's easy enough even for beginners to change the code from loading from a URL to a local path.
|
Joe Cat |
Nov 30, 2023 |
| PDF |
Page 317
code in the end |
```
# Select all elements of a vector
vector[:]
array([1, 2, 3, 4, 5, 6])
```
The result should be tensor, instead of array.
|
HIDEMOTO NAKADA |
May 01, 2024 |
| PDF |
Page 321
solution code |
# Create a two-dimensional tensor
tensor = torch.tensor([[[1,2,3]]])
while the comment says 'two-dimensional' ,
the code actually creates a three-dimensional tensor,
and thus, the results following the code are also affected.
this should be
tensor = torch.tensor([[1,2,3]])
|
HIDEMOTO NAKADA |
May 02, 2024 |