Building Probabilistic Graphical Models with Python
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I have to admit up front that this book is out of my league.
The Python is sensible to me. The subject matter  graph models, learning and inference  is above my pay grade.
Asking About a Book
Let me summarize before diving into details.
Asking someone else if a book is useful is really not going to reveal much. Their background is not my background. They found it helpful/confusing/incomplete/boring isn't really going to indicate anything about how I'll find it.
Asking someone else for a vague, unmeasurable judgement like "useful" or "appropriate" or "helpful" is silly. Someone else's opinions won't apply to you.
Asking if a book is technically correct is more measurable. However. Any competent publisher has a thorough pipeline of editing. It involves at least three steps: Acceptance, Technical Review, and a Final Review. At least three. A good publisher will have multiple technical reviewers. All of this is detailed in the front matter of the book.
Asking someone else if the book was technically correct is like asking if it was reviewed: a silly question. The details of the review process are part of the book. Just check the front matter online before you buy.
It doesn't make sense to ask judgement questions. It doesn't make sense to ask questions answered in the front matter. What can you ask that might be helpful?
I think you might be able to ask completeness questions. "What's omitted from the tutorial?" "What advanced math is assumed?" These are things that can be featured in online reviews.
Sadly, these are not questions I get asked.
Irrational Questions
A colleague had some questions about the book named above. Some of which were irrational. I'll try to tackle the rational questions since emphasis my point on ways not to ask questions about books.
This is a definite maybe situation. The concept of "solidifying" as expressed here bothers me a lot.
Solid mathematics  to me  means solid mathematics. Outside any code considerations. I failed a math course in college because I tried to convert everything to algorithms and did not get the math part. A kindly professor explained that "F" very, very clearly. A life lesson. The math exists outside any implementation.
I don't think code can ever "solidify" the mathematics. It goes the other way: the code must properly implement the mathematical concepts. The book depends on scipy, and scipy is a really good implementation of a great deal of advanced math. The implementation of the math sits squarely on the rocksolid foundation of scipy. For me, that's a ringing endorsement of the approach.
If the book reinvented the algorithms available in scipy, that would be reason for concern. The book doesn't reinvent that wheel: it uses scipy to solve problems.
4. Can the code be used to build prototypes?
Um. What? What does the word prototype mean in that question? If we use the usual sense of software prototype, the answer is a trivial "Yes." The examples are prototypes in that sense. That can't be what the question means.
In this context the word might mean "model". Or it might mean "prototype of a model". If we reexamine the question with those other senses of prototype, we might have an answer that's not trivially "yes." Might.
When they ask about prototype, could they mean "model?" The code in the book is a series of models of different kinds of learning. The models are complete, consistent, and work. That can't be what they're asking.
Could they mean "prototype of a model?" It's possible that we're talking about using the book to build a prototype of a model. For example, we might have a large and complex problem with several more degrees of freedom than the text book examples. In this case, perhaps we might want to simplify the complex problem to make it more like one of the text book problems. Then we could use Python to solve that simplified problem as a prototype for building a final model which is appropriate for the larger problem.
In this sense of prototype, the answer remains "What?" Clearly, the book solves a number of simplified problems and provides code samples that can be expanded and modified to solve larger and more complex problems.
To get past the trivial "yes" for this question, we can try to examine this in a negative sense. What kind of thing is the book unsuitable for? It's unsuitable as a final implementation of anything but the six problems it tackles. It can't be that "prototype" means "final implementation." The book is unsuitable as a tutorial on Python. It's not possible this is what "prototype" means.
Almost any semantics we assign to "prototype" lead to an answer of "yes". The book is suitable for helping someone build a lot of things.
Summary
Those two were the rational questions. The irrational questions made even less sense.
Including the other irrational questions, it appears that the real question might have been this.
Q: "Can I learn Python from this book?"
A: No.
It's possible that the real question was this:
Q: "Can I learn advanced probabilistic modeling with this book?"
A: Above my pay grade. I'm not sure I could learn probabilistic modeling from this book. Maybe I could. But I don't think that I have the depth required.
It's possible that the real questions was this:
Q: Can I learn both Python and advanced probabilistic modeling with this book?"
A: Still No.
Gaps In The Book
Here's what I could say about the book.
You won't learn much Python from this book. It assumes Python; it doesn't tutor Python. Indeed, it assumes some working scipy knowledge and a scipy installation. It doesn't include a quickstart tutorial on scipy or any of that other handholding.
This is not even a quibble with the presentation. It's just an observation: the examples are all written in Python 2. Small changes are required for Python 3. Scipy will work with Python 3. http://www.scipy.org/scipylib/faq.html#donumpyandscipysupportpython3x. Reworking the examples seems to involve only small changes to replace print statements. In that respect, the presentation is excellent.
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