АрртемПримеры тут можно взять: http://examples.oreilly.com/9780596158118/
У кого есть приложения с дика к книге М. Лутц “Изучаем Python” 4-е издание 2011г. поделитесь пожалуйста.
Полная версия: Книги по python
igor, спасибо за ссылку. Это ссылка на приложение к книге “Програмирование на Python”. К сожалению, до нее еще не дорос.
Вот ссылка на приложение к книге “Изучаем Python” 4-е издание 2011г. (воспользовался направлением заданным igor, еще раз спасибо)
http://examples.oreilly.com/9780596158071/
Кстати, уже вышло 5-е издание “Изучаем Python” для версий 2.7 и 3.3 (правда пока на английском)
http://shop.oreilly.com/product/0636920028154.do
Вот ссылка на приложение к книге “Изучаем Python” 4-е издание 2011г. (воспользовался направлением заданным igor, еще раз спасибо)
http://examples.oreilly.com/9780596158071/
Кстати, уже вышло 5-е издание “Изучаем Python” для версий 2.7 и 3.3 (правда пока на английском)
http://shop.oreilly.com/product/0636920028154.do
Привет ребята.
Подскажите основные отличия серий книг “изучаем” и “программируем” от М.Лутца.
Есть шанс купить по-дешевле через знакомого в Москве, нужно определится что брать.
“Программируем” вообще в 2 частях .
Подскажите основные отличия серий книг “изучаем” и “программируем” от М.Лутца.
Есть шанс купить по-дешевле через знакомого в Москве, нужно определится что брать.
“Программируем” вообще в 2 частях .
GtrОсновное отличие, что в серии “изучаем” дан разжеваный материал для полных новичков. В 2-х томах “программируем” практическое приминение для людей уже освоивших другой язык программирования или знакомых с python.
Привет ребята.Подскажите основные отличия серий книг “изучаем” и “программируем” от М.Лутца.Есть шанс купить по-дешевле через знакомого в Москве, нужно определится что брать.“Программируем” вообще в 2 частях .
Какой-то старенький сборник: самая молодая книга датирована 2008годом, жаль конечно, но за неимением лучшего буду читать то, что есть и help(модуль). 
moronНа предыдущей странице ссылка на Лутца (2011г. изд.) актуально до сих пор.
Какой-то старенький сборник: самая молодая книга датирована 2008годом, жаль конечно, но за неимением лучшего буду читать то, что есть и help(модуль).
ZeUsMУже треть прочитал “Изучаем Python”. Очень доходчиво пишет. Особенно нравятся комментарии по поводу скорости выполнения тех или иных команд или алгоритмов. Примеры с объяснениями чем плох или хорош данный код. Таким образом Лутц как бы в самом зародыше мышления на языке Python направляет в сторону хорошего продуманного кода.
На предыдущей странице ссылка на Лутца (2011г. изд.) актуально до сих пор.
Бесплатная книга Bayesian Methods for Hackers
Bayesian Methods for Hackers
Using Python and PyMC
The Bayesian method is the natural approach to inference, yet it is hidden from readers behind chapters of slow, mathematical analysis. The typical text on Bayesian inference involves two to three chapters on probability theory, then enters what Bayesian inference is. Unfortunately, due to mathematical intractability of most Bayesian models, the reader is only shown simple, artificial examples. This can leave the user with a so-what feeling about Bayesian inference. In fact, this was the author's own prior opinion.
After some recent success of Bayesian methods in machine-learning competitions, I decided to investigate the subject again. Even with my mathematical background, it took me three straight-days of reading examples and trying to put the pieces together to understand the methods. There was simply not enough literature bridging theory to practice. The problem with my misunderstanding was the disconnect between Bayesian mathematics and probabilistic programming. That being said, I suffered then so the reader would not have to now. This book attempts to bridge the gap.
If Bayesian inference is the destination, then mathematical analysis is a particular path to towards it. On the other hand, computing power is cheap enough that we can afford to take an alternate route via probabilistic programming. The latter path is much more useful, as it denies the necessity of mathematical intervention at each step, that is, we remove often-intractable mathematical analysis as a prerequisite to Bayesian inference. Simply put, this latter computational path proceeds via small intermediate jumps from beginning to end, where as the first path proceeds by enormous leaps, often landing far away from our target. Furthermore, without a strong mathematical background, the analysis required by the first path cannot even take place.
Bayesian Methods for Hackers is designed as a introduction to Bayesian inference from a computational/understanding-first, and mathematics-second, point of view. Of course as an introductory book, we can only leave it at that: an introductory book. For the mathematically trained, they may cure the curiosity this text generates with other texts designed with mathematical analysis in mind. For the enthusiast with less mathematical-background, or one who is not interested in the mathematics but simply the practice of Bayesian methods, this text should be sufficient and entertaining.
The choice of PyMC as the probabilistic programming language is two-fold. As of this writing, there is currently no central resource for examples and explanations in the PyMC universe. The official documentation assumes prior knowledge of Bayesian inference and probabilistic programming. We hope this book encourages users at every level to look at PyMC. Secondly, with recent core developments and popularity of the scientific stack in Python, PyMC is likely to become a core component soon enough.
PyMC does have dependencies to run, namely NumPy and (optionally) SciPy. To not limit the user, the examples in this book will rely only on PyMC, NumPy, SciPy and Matplotlib only.
Bayesian Methods for Hackers
Using Python and PyMC
The Bayesian method is the natural approach to inference, yet it is hidden from readers behind chapters of slow, mathematical analysis. The typical text on Bayesian inference involves two to three chapters on probability theory, then enters what Bayesian inference is. Unfortunately, due to mathematical intractability of most Bayesian models, the reader is only shown simple, artificial examples. This can leave the user with a so-what feeling about Bayesian inference. In fact, this was the author's own prior opinion.
After some recent success of Bayesian methods in machine-learning competitions, I decided to investigate the subject again. Even with my mathematical background, it took me three straight-days of reading examples and trying to put the pieces together to understand the methods. There was simply not enough literature bridging theory to practice. The problem with my misunderstanding was the disconnect between Bayesian mathematics and probabilistic programming. That being said, I suffered then so the reader would not have to now. This book attempts to bridge the gap.
If Bayesian inference is the destination, then mathematical analysis is a particular path to towards it. On the other hand, computing power is cheap enough that we can afford to take an alternate route via probabilistic programming. The latter path is much more useful, as it denies the necessity of mathematical intervention at each step, that is, we remove often-intractable mathematical analysis as a prerequisite to Bayesian inference. Simply put, this latter computational path proceeds via small intermediate jumps from beginning to end, where as the first path proceeds by enormous leaps, often landing far away from our target. Furthermore, without a strong mathematical background, the analysis required by the first path cannot even take place.
Bayesian Methods for Hackers is designed as a introduction to Bayesian inference from a computational/understanding-first, and mathematics-second, point of view. Of course as an introductory book, we can only leave it at that: an introductory book. For the mathematically trained, they may cure the curiosity this text generates with other texts designed with mathematical analysis in mind. For the enthusiast with less mathematical-background, or one who is not interested in the mathematics but simply the practice of Bayesian methods, this text should be sufficient and entertaining.
The choice of PyMC as the probabilistic programming language is two-fold. As of this writing, there is currently no central resource for examples and explanations in the PyMC universe. The official documentation assumes prior knowledge of Bayesian inference and probabilistic programming. We hope this book encourages users at every level to look at PyMC. Secondly, with recent core developments and popularity of the scientific stack in Python, PyMC is likely to become a core component soon enough.
PyMC does have dependencies to run, namely NumPy and (optionally) SciPy. To not limit the user, the examples in this book will rely only on PyMC, NumPy, SciPy and Matplotlib only.
http://takepdf.com
пока есть следующие
Beginning-Python-Using-Python-2.6-and-Python-3.1—Wrox.pdf
_Rapid_GUI_Programming_with_Pyth.pdf
MongoDB_Python.pdf
Paul_Berry_-_Head_First_Python_(2010__PDF).tar.bz2
Python-2.6-Graphics-Cookbook.pdf
Python-3-OOP.pdf
Python-Algorithms—Apress.pdf
Python_for_Unix_and_Linux.pdf
Python-Testing-Beginners-Guide—Packtpub.pdf
Марк Лутц - Изучаем Python - 4-е изд - 2011.djvu
пока есть следующие
Beginning-Python-Using-Python-2.6-and-Python-3.1—Wrox.pdf
_Rapid_GUI_Programming_with_Pyth.pdf
MongoDB_Python.pdf
Paul_Berry_-_Head_First_Python_(2010__PDF).tar.bz2
Python-2.6-Graphics-Cookbook.pdf
Python-3-OOP.pdf
Python-Algorithms—Apress.pdf
Python_for_Unix_and_Linux.pdf
Python-Testing-Beginners-Guide—Packtpub.pdf
Марк Лутц - Изучаем Python - 4-е изд - 2011.djvu