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Djangoweekly has a listing of all published Django books on one page. Note check publication date and which version of Django the book is using.
The short answer is: because it’s the actively-developed version of the language, and you should use it for the same reason you’d use 2.7 instead of 2.6. If you’re here, I’m guessing that’s not enough. You need something to sweeten the deal. Well, friend, I have got a whole mess of sugar cubes just for you.
Series of Python Videos by Josh Gordon of Google teaching Machine learning basics.
Pythonistas praise a good API by calling it “Pythonic.” That quality has much to do with proper use of the special methods used in the Python Data model, which define the essential behaviors that we expect in objects. Perhaps you’ve used Python for years. Do you really know it? This tutorial is intended for a Python programmer who has working/practical knowledge of the language plus an understanding of object-oriented programming, who now needs to learn how to write idiomatic APIs
The Django Signals is a strategy to allow decoupled applications to get notified when certain events occur. Let’s say you want to invalidate a cached page everytime a given model instance is updated, but there are several places in your code base that this model can be updated. You can do that using signals, hooking some pieces of code to be executed everytime this specific model’s save method is trigged.
In this tutorial I will present you the built-in signals and give you some general advices about the best practices.
Learn how to write behavioral tests for your next Python application using the Behave library.
Full text search for PostgreSQL. New-style middleware to solve the lack of strict request/response layering of the old-style of middleware. Official support for Unicode usernames. Check release notes for more info.
You may have heard of this awesome tool called OnionScan that is used to scan hidden services in the dark web looking for potential data leaks. Recently the project released some cool visualizations and a high level description of what their scanning results looked like. What they didn’t provide is how to actually go about scanning as much of the dark web as possible, and then how to produce those very cool visualizations that they show.
The Python Software Foundation recognized Batchelder with a Community Service Award for his tireless work helping run the Boston Python user group, being a regular speaker at conferences, maintaining coverage.py, and being a friendly face for the community on IRC and elsewhere
The multiprocessing module was added to Python in version 2.6. It was originally defined in PEP 371 by Jesse Noller and Richard Oudkerk. The multiprocessing module allows you to spawn processes in much that same manner than you can spawn threads with the threading module. The idea here is that because you are now spawning processes, you can avoid the Global Interpreter Lock (GIL) and take full advantages of multiple processors on a machine.
In this tutorial, I will go over how to setup a Django Channels project to work with Celery and have instant notification when task starts and completes. Django Channels uses WebSockets to enable two-way communication between the server and browser client. It is assumed that the reader is comfortable with how to setup a normal Django project and we will only cover the parts relating to Channels and Celery.
Despite all the recent buzz about deep learning, the design and testing of a neural network pipeline may become a task for developers who aren't machine learning specialists. This tutorial is intended for a software developer who has intermediate experience in Python, plus some hands-on experience developing data pipelines and working with machine learning use cases, who now needs to learn how to build high-performance classifiers based on deep learning.
I just finished the performance optimized pure Python implementation of my RFC3339 date-time library udatetime for PyPy and Python 3.5. The benchmark say PyPy is now officially the fastest with udatetime. Again it’s astonishing how good PyPy performs.
Dask core contributor Jim Crist has put together a series of posts discussing some recent experiments combining Dask and scikit-learn on his blog, Marginally Stable. The tutorial spans three posts, which covers model parallelism, data parallelism and combining the two with a real-life dataset.
By Vasudev RamHere's a simple Python program that shows how the os.kill function from Python's standard library, along with the os.getpid function and the signal module , can be used to terminate the current program - the one it is called from:'''Program: test_self_kill.pyA program to show that the os.kill function can be used to terminate the current program.Author: Vasudev RamCopyright 2016 Vasudev Ramhttps://vasudevram.github.iohttp://jugad2.blogspot.comhttps://gumroad.com/vasudevram'''from __future__ import print_functionimport sys, os, signalprint("Python version:", sys.version)print("This line will be printed.")os.kill(os.getpid(), signal.SIGTERM)print("If os.kill works, this line w...
The “Python’s Innards” series owes its existence, at least in part, to hearing one of the Python-Fu masters in my previous workplace say something about a switch statement so large that it was needed to break it up just so some compilers won’t choke on it. I remember thinking then: “Choke the compiler with a switch? Hrmf, let me see that code.” Turns out that this switch can be found in ./Python/ceval.c.
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Keras code and weights files for popular deep learning models.
Python package to visualize some processes involved in Machine learning.
Standalone python program that draws a triangle using vulkan on Windows and Linux
Get exact IVs for Pokemon Go
Donald Trump's entire Twitter feed and the script used to scrape it.
Implements a minimalist directed graph database in memory via Python3