Python Weekly (Issue 473 November 5 2020)

Python Weekly - Issue 473

November 05, 2020

Python Weekly

Welcome to issue 473 of Python Weekly. Let's get straight to the links this week.

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A weekly newsletter featuring the best hand curated news, articles, tutorials, talks, tools and libraries etc for programmers.

Articles, Tutorials and Talks

We'll start all the way from the very basics (reading images and video, image transformations, drawing on images) to the more advanced concepts (color spaces, edge detection, and thresholding). Towards the end, we'll be building a Deep Computer Vision model to detect between the characters in "The Simpsons". 

The focus in this Understand Django article is how to manage users in your Django application. We'll study Django's built-in user authentication system.

The goal of this post is to explain how CPython does what we tell it to do, that is, how it executes the bytecode to which the code we write compiles.

In this article, we'll dive into advanced django tips, including Q objects, annotations, prefetch objects, custom querysets, and custom model managers.

This article will focus on how Deepnote’s Jupyter-backed notebook environment and fast.ai’s effective encapsulation of machine learning data preparation greatly improve data scientist efficiency.

AprilTags are a type of fiducial marker. Fiducials, or more simply “markers,” are reference objects that are placed in the field of view of the camera when an image or video frame is captured. In this tutorial, you will learn how to perform AprilTag detection with Python and the OpenCV library.

Let’s get curated data, natural language processing, text-to-speech, plot rendering and more from Wolfram Alpha into your next project!

As a data scientist or an analyst, the quality of the data you work with is crucial to your success. The old GIGO acronym, which stands for “garbage in, garbage out”, is very true. In this blog post, we’ll discuss methods and practices that will make your first contact with data successful and will save you from a lot of grief down the road.

Random Forest is a popular and effective ensemble machine learning algorithm. In this tutorial, you will discover how to develop a Random Forest model for time series forecasting.

Interesting Projects, Tools and Libraries

A simple flow-based visual scripting runtime environment for Python, serving as a diagram-like representation of your Python script while being executable at the same time.

Auto fix Django code smells in your pull requests.

A music theory development framework in Python.

An open source security tool for testing a data center's resiliency to perimeter breaches and internal server infection. The Monkey uses various methods to self propagate across a data center and reports success to a centralized Monkey Island server.

A library for publication, discovery, and consumption of State-of-the-art models to turn data into vectors. (text2vec, image2vec, video2vec, graph2vec, bert, inception, etc)

Pynguin, the PYthoN General UnIt test geNerator, is a tool that allows developers to generate unit tests automatically.

A personal organization software with a script engine for automation.

A multi-party collaborative machine learning framework.

A container for dataclasses with multi-indexing and bulk operations.

Command line tool for improving typing skills (programmers friendly)

Extends python lists with LINQ functionality for clean, fast coding.

Code Video Generator is a library that uses the Manim animation engine to automatically generate code walkthrough videos. 

New Releases

Upcoming Events and Webinars

PyData Global 2020 is the very first fully-online PyData conference. Join our global community for five days packed with talks, tutorials, posters, open-source sprints and a digital hallway track.

PyCon SE 2020 will be held completely online. We will stream all talks live via our YouTube channel for free.

There will be following talks

  • Social Dealings with Python

  • You Only Look Once (YOLO), a new computer vision technique

  • Crossword Puzzle generation using pyqt5 

There will be following talks

  • Github Actions 

  • Infrastructure (Diagrams!) as Code

There will be following talks 

  • Giving the gift of your code

  • Deploying Computer Vision Models to the Edge in the Wild

There will be following talks 

  • PySpark : Combining Machine Learning & Big Data 

  • A pandas-like API for PySpark

There will be following talks 

  • Build a standalone desktop app using Flask & Electron

  • Fishbowl Discussion of Electron

There will be a talk, Data-Driven Development using Feature Flags.

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