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 |
| ePub |
Page 1
Figure 2-17 |
Just wanted to share with you something that I think is a mistake in Figure 2-17.
Ss it is said in the paragraph following the figure, this figure plots information in data space. So, if letter x refers to variables (which, from the formula placed in the figure, it does), the axis of this figure should not be labeled with x, but with the names of the observations (as done in Figure 2-2). Which should be effectively labelled as X1, X2, ... Xm are the dots.
|
Ramiro Heraclio |
Apr 15, 2018 |
| Printed |
Page 3
3rd paragraph |
Under the Features section, authors have mentioned, “A feature is a numeric representation of raw data.” Nevertheless, features can also be categorical.
Is it supposed to be “A feature is a representation of raw data.”
|
Manoj Jayabalan |
Mar 11, 2019 |
| PDF |
Page 3
Last Paragraph |
If this was an overview from 30,000 feet, just about scrapes the surface OK.
Misses the fact that the activitiy has a buisness purpose, in that there is a problem to solve and a target to show effectiveness, either against some existing technique or approach or to what the AI/ML solution is supposed to find out. It is fun to run the numbers but the point is to bring more efficient or better insight into a problem from the data.
|
Geoffrey Leigh |
Jan 09, 2020 |
| PDF |
Page 61
3rd paragraph |
In Chapter 4 an unusual variant of tf*idf is described - with tf being raw word counts. This is not a default in most definitions; adjusting tf by document length is much more common. I'm not aware of implementations where the presented tf*idf variant is a default; in scikit-learn, NLTK, gensim, ElasticSearch or "Recommended tf–idf weighting schemes" in Wikipedia tf is normalized by the document length by default.
This is not a problem on its own; the problem is that while analysis in Chapter 4 is valid for the presented tf*idf variant, it can't be generalized for more commonly used tf*idf variants. This makes statements about tf*idf misleading, e.g. "Tf-Idf = Column Scaling" is not true in most cases. More importantly, the analysis of tf*idf effect ("Deep Dive: What Is Happening?", p72-75) doesn't hold if one of the "default" tf*idf variants is used, as they're not just a column scaling.
On p65 there is a note: "Note that doing tf-idf then l2 normalization is the same as doing l2 normalization alone." While technically correct, it is then illustrated with a code sample: a result of
text.TfidfTransformer(norm=None).transform is column-wise L2-normalized. This is somewhat misleading, because the default text.TfidfTransformer(norm='l2') is doing a very different kind of normalization: rows are normalized, not columns. From the description it sounds like changing a default value of `norm` argument is just a way to get both normalized and unnormalized tf*idf results without calling TfidfTransformer twice, but that's not the case: default text.TfidfTransformer() is doing a completely different computation, which changes all the following analysis - scaling is not column-wise.
I think Chapter 4 should have used IDF scaling as an example, not TF*IDF, or make it clear that analysis doesn't hold for default/common TF*IDF implementations people will be using in practice.
|
Mikhail Korobov |
Aug 28, 2018 |
| Printed |
Page 104
Equation 6-7, near the bottom |
Objective function for principal components, matrix-vector formulation, should be
wtXtXw
(where "t" means "transpose") instead of
wtw
The same goes for Equation 6-8 in the next page.
|
Anonymous |
Dec 11, 2020 |
| Printed |
Page 104
Equation 6-7 |
The objective function for principal components, matrix-vector formation should be maximizing w for z'z instead of w'w (where ' is used to indicate a transpose). Same with equation 6-8 on the next page (105)
You cannot maximize w for w'w much as w is constrained to be a unit vector.
Though it's most probably a typo, it changes the entire mathematical meaning of the objective function.
|
Anonymous |
Feb 14, 2021 |
| PDF |
Page 136
last paragraph |
Figure 8-3 illustrates examples ......
The center image contains vertical stripes; therefore, the horizontal gradient is zero.
To be changed:
therefore, the vertical gradient is zero.
* horizontal stripes >>> horizontal gradient is zero
* vertical stripes >>> vertical gradient is zero
|
Woohyun Kim |
Aug 29, 2018 |
| PDF |
Page 163
1st row, 2nd row |
The code does not work, as the defined feature_array on page 162 requires 3 arguments, but only 2 are provided, here.
|
Anonymous |
Mar 06, 2021 |