Learning Python: Powerful Object-Oriented Programming (4th by Mark Lutz

By Mark Lutz

Google and YouTube use Python simply because it's hugely adaptable, effortless to take care of, and enables fast improvement. so that it will write top of the range, effective code that's simply built-in with different languages and instruments, this hands-on ebook may also help you be effective with Python fast -- no matter if you're new to programming or simply new to Python. It's an easy-to-follow self-paced educational, in line with writer and Python specialist Mark Lutz's renowned education path.

every one bankruptcy incorporates a stand-alone lesson on a key element of the language, and contains a specified Test Your Knowledge part with sensible workouts and quizzes, so that you can perform new talents and attempt your figuring out as you move. You'll locate plenty of annotated examples and illustrations that can assist you start with Python 3.0.

* know about Python's significant integrated item varieties, similar to numbers, lists, and dictionaries
* Create and method items utilizing Python statements, and study Python's normal syntax version
* constitution and reuse code utilizing services, Python's uncomplicated procedural instrument
* find out about Python modules: applications of statements, capabilities, and different instruments, geared up into greater parts
* notice Python's object-oriented programming instrument for structuring code
* find out about the exception-handling version, and improvement instruments for writing higher courses
* discover complex Python instruments together with decorators, descriptors, metaclasses, and Unicode processing

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Extra resources for Learning Python: Powerful Object-Oriented Programming (4th Edition)

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This shows how fast the ROC grows in its initial stage. Results at the “image-level of detail" (for whole C-scans) are reported in the Table 2. 5 sensitivity. This operation was done in order to significantly decrease the number of false alarms in images, but still keeping high detectivity. 999, assuming an independence model for simplification. The following summarizing comments on the results can be formulated. Obviously, our data set is strongly imbalanced, favoring the negative class, and so are classification results.

It is sufficient to look to a usual fuzzy control system output that may be neither normal nor convex. In this paper we propose to substitute a given fuzzy number with an interval which has some properties like to be the nearest in some sense we describe. The results we present start from a paper of Grzegorzewski [10] in which he proposes the nearest interval to the original fuzzy number with respect to several distances. In particular we have focused on the distance introduced by Trutschnig et al.

In our case study, we worked with a fairly limited collection of 210 C-scans (from ≈1 m2 each), but we generated a large data set from it—with approximately 7 × 104 examples of 3D windows each described by over 15 thousand features. Our experiments showed that a commonly applied single-feature weak learner which approximates class conditional probabilities by normal distributions (popularly known as “real boost," NRB in our notation) was significantly outperformed by learners only slightly more refined—based on binning and shallow decision trees.

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