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Programming With Python and Gen

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Development
  • Python list comprehension with Examples

    This tutorial covers how list comprehension works in Python. It includes many examples which would help you to familiarize the concept and you should be able to implement it in your live project at the end of this lesson.

  • Ibis: Python data analysis productivity framework

    Ibis is a library pretty useful on data analysis tasks that provides a pandas-like API that allows operations like create filter, add columns, apply math operations etc in a lazy mode so all the operations are just registered in memory but not executed and when you want to get the result of the expression you created, Ibis compiles that and makes a request to the remote server (remote storage and execution systems like Hadoop components or SQL databases). Its goal is to simplify analytical workflows and make you more productive.

  • Reasons Why Python is Good for AI and ML

    Artificial Intelligence (AI) and Machine Learning (ML) are the new black of the IT industry. While discussions over the safety of its development keep escalating, developers expand abilities and capacity of artificial intellect. Today Artificial Intelligence went far beyond science fiction idea. It became a necessity. Being widely used for processing and analyzing huge volumes of data, AI helps to handle the work that cannot be done manually anymore because of its significantly increased volumes and intensity.

  • The Python Software Foundation is looking for bloggers!

    The Python Software Foundation (PSF) is looking to add bloggers for the PSF blog located at http://pyfound.blogspot.com/. As a PSF blogger, you will work with the PSF Communication Officers to brainstorm blog content, communicate activities, and provide updates on content progression. Example of content includes PSF community service awardee profiles, details about global Python events and PSF grants, or recent goings-on within the PSF itself. One goal of the 2019 - 2020 PSF Board of Directors is to increase transparency around PSF activities by curating more frequent blog content.

  • Racket is an acceptable Python

    A little over a decade ago, there were some popular blogposts about whether Ruby was an acceptable Lisp or whether even Lisp was an acceptable Lisp. Peter Norvig was also writing at the time introducing Python to Lisp programmers. Lisp, those in the know knew, was the right thing to strive for, and yet seemed unattainable for anything aimed for production since the AI Winter shattered Lisp's popularity in the 80s/early 90s. If you can't get Lisp, what's closest thing you can get?

    This was around the time I was starting to program; I had spent some time configuring my editor with Emacs Lisp and loved every moment I got to do it; I read some Lisp books and longed for more. And yet when I tried to "get things done" in the language, I just couldn't make as much headway as I could with my preferred language for practical projects at the time: Python.

    Python was great... mostly. It was easy to read, it was easy to write, it was easy-ish to teach to newcomers. (Python's intro material is better than most, but my spouse has talked before about some major pitfalls that the Python documentation has which make getting started unnecessarily hard. You can hear her talk about that at this talk we co-presented on at last year's RacketCon. I'll leave that to her to discuss at some point however.) I ran a large free software project on a Python codebase, and it was easy to get new contributors; the barrier to entry to becoming a programmer with Python was low. I consider that to be a feature, and it certainly helped me bootstrap my career.

    Most importantly of all though, Python was easy to pick up and run with because no matter what you wanted to do, either the tools came built in or the Python ecosystem had enough of the pieces nearby that building what you wanted was usually fairly trivial.

  • Pipx – Install And Run Python Applications In Isolated Environments

    It is always recommended to install Python applications in Virtual Environments to avoid conflicts with one another. Pip package manager helps us to install Python applications in an isolated environments, using two tools namely venv and virtualenv. There is also another Python package manager named “Pipenv”, which is recommended by Python.org, to install Python applications. Unlike Pip, Pipenv automatically creates virtual environments by default. Meaning – you don’t need to manually create virtual environments for your projects anymore. Today, I stumbled upon a similar tool named “Pipx”, a free and open source utility that allows you to install and run Python applications in an isolated virtual environments.

    Using Pipx, we can easily install thousands of Python applications hosted in PyPI without much hassle. Good thing is you can do everything with regular user permissions. You need not to be “root” user or need not to have “sudo” permissions. It is worth mentioning that Pipx can run a program from temporary environment, without having to install it. This will be handy when you test multiple versions of same program often. The packages installed with Pipx can be listed, upgrade or uninstalled at any time. Pipx is a cross-platform application, so it can run on Linux, Mac OS and Windows.

  • Check-in #7: (5 July - 11 July)
  • PSF GSoC students blogs: Seventh Week [July 1st - July 7th] [3rd PSF Blog Post]
  • Python for NLP: Creating TF-IDF Model from Scratch

    This is the 14th article in my series of articles on Python for NLP. In my previous article, I explained how to convert sentences into numeric vectors using the bag of words approach. To get a better understanding of the bag of words approach, we implemented the technique in Python.

    In this article, we will build upon the concept that we learn in the last article and will implement the TF-IDF scheme from scratch in Python. The term TF stands for "term frequency" while the term IDF stands for the "inverse document frequency".

  • Highest used Python code in the Pentesting/Security world

    I think this is the highest used Python program in the land of Pentesting/Security, Almost every blog post or tutorial I read, they talk about the above-mentioned line to get a proper terminal after getting access to a minimal shell on a remote Linux server.

  • Gen: a general-purpose probabilistic programming system with programmable inference

    PLDI 2019 Proceedings of the 40th ACM SIGPLAN Conference on Programming Language Design and Implementation

  • New AI programming language goes beyond deep learning

    In a paper presented at the Programming Language Design and Implementation conference this week, the researchers describe a novel probabilistic-programming system named “Gen.” Users write models and algorithms from multiple fields where AI techniques are applied — such as computer vision, robotics, and statistics — without having to deal with equations or manually write high-performance code. Gen also lets expert researchers write sophisticated models and inference algorithms — used for prediction tasks — that were previously infeasible.

    In their paper, for instance, the researchers demonstrate that a short Gen program can infer 3-D body poses, a difficult computer-vision inference task that has applications in autonomous systems, human-machine interactions, and augmented reality. Behind the scenes, this program includes components that perform graphics rendering, deep-learning, and types of probability simulations. The combination of these diverse techniques leads to better accuracy and speed on this task than earlier systems developed by some of the researchers.

More in Tux Machines

Annual Report 2018: LibreOffice development

Throughout the second half of 2018, the developer community worked on a new major release: LibreOffice 6.2. Details about the end-user-facing new features are provided on this page, and in the following video – so in the rest of this blog post, we’ll focus on developer-related changes. Read more

Programming Leftovers

Linux Kernel: Chrome OS, Direct Rendering Manger (DRM) and Char/Misc

  • Various Chrome OS Hardware Support Improvements Make It Into Linux 5.3 Mainline

    Various Chrome OS hardware platform support improvements have made it into the Linux 5.3 kernel for those after running other Linux distributions on Chromebooks and the like as well as reducing Google's maintenance burden with traditionally carrying so much material out-of-tree.

  • The Massive DRM Pull Request With AMDGPU Navi Support Sent In For Linux 5.3

    At 479,818 lines of new code and just 36,145 lines of code removed while touching nearly two thousand files, the Direct Rendering Manger (DRM) driver updates for Linux 5.3 are huge. But a big portion of that line count is the addition of AMD Radeon RX 5000 "Navi" support and a good portion of that in turn being auto-generated header files. Navi support is ready for the mainline Linux kernel!

  • Char/Misc Has A Bit Of Changes All Over For Linux 5.3

    The char/misc changes with each succeeding kernel release seem to have less changes to the character device subsystem itself and more just a random collection of changes not fitting in other subsystems / pull requests. With Linux 5.3 comes another smothering of different changes.

today's howtos