Python Weekly (Issue 427 December 12 2019)

Python Weekly - Issue 427

Python Weekly

Welcome to issue 427 of Python Weekly. Let's get straight to the links this week.

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News

The PyTorch team has invested $250,000 to support OpenMined in furthering the development and proliferation of privacy-preserving ML and there are three different opportunities for you to participate in the project’s development. Each of these fellowships furthers our shared mission to lower the barrier-to-entry for privacy-preserving ML and to create a more privacy-preserving world.

Articles, Tutorials and Talks

Reverse engineering an unfamiliar data file could be described as the bootstrapping of understanding. In many ways the process resembles the scientific method, only applied to human-made, abstract objects instead of the natural world. You begin by gathering data, and then you use that information to put forth one or more hypotheses. You test the hypotheses, and use the outcome of those tests to refine them. Repeat as needed. Developing skills in reverse engineering is largely a matter of practice. Through an accumulation of experiences, you build up an intuition of where to investigate first, what patterns to look for, and what tools to keep handy. This post walks through the process of reverse-engineering some data files from an old computer game, in order to show you a little bit of how it's done.

There are plenty of articles written about lots of cool features in Python such as variable unpacking, partial functions, enumerating iterables, but there is much more to talk about when it comes to Python, so here I will try to show some of the features I know and use, that I haven't yet seen mentioned elsewhere.

  • Part 2 - Here comes another round of Python features that you hopefully haven't seen yet.

A Deep-Dive look at the Math of “Extended Warranties”

Uber AI's Plug and Play Language Model gives NLP practitioners the flexibility to plug simple attribute models into a large, unconditional language model.

A comprehensive tutorial on getting started with Tesseract and OpenCV for OCR in Python: preprocessing, deep learning OCR, text extraction and limitations.

A collection of insights and recommendations to help data and analytics leaders overcome the challenges of hiring and managing data science teams. This Gartner report is provided by Domino Data Lab.

SPONSORPlotnine: Grammar of Graphics for PythonA translation of the visualisation chapters from “R for Data Science” to Python using Plotnine and Pandas.Writing a Python ORM for RedisAn elegant way to store instances in Redis.Django 3.0 Full Course For Beginners Doing Python Configuration RightNorth Bay Python 2019 Videos shrynk - Using Machine Learning to learn how to CompressMonads aren't as hard as you think Interesting Projects, Tools and LibrariesAI Dungeon 2AI Dungeon 2 is a completely AI generated text adventure built with OpenAI's largest GPT-2 model. It's a first of it's kind game that allows you to enter and will react to any action you can imagine.Mario: Shell pipes in PythonHave you ever wanted to use Python functions directly in your Unix shell? Mario can read and write csv, json, and yaml; traverse trees, and even do xpath queries. Plus, it supports async commands right out of the box. Build your own commands with a simple configuration file, and install plugins for even more!Informer A Telegram Mass Surveillance Bot in Python.awspxA graph-based tool for visualizing effective access and resource relationships in AWS environments.PixcryptionPixcryption's goal is to offer a new form of steganography/encryption through imagery. It uses a random seeded UUID to generate a user_key which matches RGB perfect values to match to unicode characters. These are stored in a user_key.png file which is used to encrypt and decrypt messages.PyFlow Visual scripting framework for Python.pydepsPython module dependency visualization.video-to-pose3DConvert video to 3D pose in one-key.omnibotA Slack proxy, and Slack bot framework. GoCheese A Python private package repository and caching proxy.HorologyConveniently measures time of your for-loops, contexts and functions.Maze-GeneratorA script that generates randomized mazes/labrynths. Invented by accident.SparkTorchTrain and run Pytorch models on Apache Spark.django_vue_generatorGenerates vue frontend structure for django rest framework projects. Generates forms(validated with vuelidate) for your serializers and viewsets and methods to call api via vue-resource.PyTorch ElasticPyTorch Elastic (torchelastic) is a framework that enables distributed training jobs to be executed in a fault tolerant and elastic manner. blender-toolsA Blender add-on containing workflow tools for game development.New ReleasesPython 3.8.1rc1Upcoming Events and WebinarsPresentation Night @ Boston Python Meetup - Cambridge, MAThere will be following talks 

  • Python Optimization Options

  • Extending Python with Rust

Optimized human learning - London, UKSpaced repetition, the testing of knowledge over a period of time, is one of the most important tools we know of in the science of learning. Modern technology, in the form of spaced repetition software like Anki, promise to take this to the next level by offering personalized learning schedules for each individual learner — but often underdeliver. In this talk, Jacob Puthipiroj walks through an implementation of spaced repetition scheduling via machine learning, the first of its kind in a commercially available software. Lightning Talks @ NYC PyLadies - New York, NYPyHou Meetup December 2019 - Houston, TXSan Diego Python Meetup December 2019 - San Diego, CALjPyMeetup December 2019 - Ljubljana, Slovenia 

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