Python Weekly (Issue 423 November 14 2019)

Python Weekly - Issue 423

November 14, 2019

Python Weekly

Welcome to issue 423 of Python Weekly. Let's get straight to the links this week.

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News

Although GitHub has traditionally been home to software developers, the world’s code is evolving. Behind Python’s growth is a speedily-expanding community of data science professionals and hobbyists—and the tools and frameworks they use every day. These include the many core data science packages powered by Python that are both lowering the barriers to data science work and proving foundational to projects in academia and companies alike.

The Python Software Foundation Packaging Working Group is receiving funding to work on the design, implementation, and rollout of pip's next-generation dependency resolver. They are looking to hire two contractors, a senior developer and an intermediate developer, to work on development, testing and building test infrastructure, code review, bug triage, and assisting in the rollout of necessary features.

Jupyter Community Workshops bring together small groups of Jupyter community members and core contributors for high-impact strategic work and community engagement on focused topics. Our vision is that the events funded in this round would occur no later than August of 2020.

Articles, Tutorials and Talks

In 2018 I moved to Ireland from the United States, and the house I rented was very cold, in spite of having a modern Internet-enabled heating controller. In this talk I want to tell you how I "debugged" the problem and how I then improved the heating using a couple of microcontrollers running MicroPython, all without making any modifications to this house I do not own.

From GPS navigation to network-layer link-state routing, Dijkstra’s Algorithm powers some of the most taken-for-granted modern services. Utilizing some basic data structures, let’s get an understanding of what it does, how it accomplishes its goal, and how to implement it in Python (first naively, and then with good asymptotic runtime!)

In this video, I use convolutional neural networks--written in Python with the help of Tensorflow and Keras--to make a handwritten digit calculator. In doing so, I dive deep into how convolutional neural networks do what they do.

Mastering the basics means mastering the craft. This step-by-step guide will teach you how to successfully build APIs.

Bring the best practices established by the Python community to your Jupyter Notebooks.  You can add virtual environments to Jupyter Lab, giving each notebook it's own environment.  This post goes into detail explaining exactly how you can add virtual environments to your own notebooks on Google Cloud's AI Platform Notebooks.

This post explains (using a very simple example) how we implemented a view-based permissions system using Django and Django REST Framework, and attempts to justify why we chose to do such a thing.

This article shows how to spin up a quick and dirty search engine on your own local machine using fscrawler, Elasticsearch, Python and Flask.

It isn't always easy to see where the performance bottlenecks of your code are. This tutorial will introduce you to the tools that are available to profile your code and to measure the effect that optimising your program has.

Register now for this hands-on code webinar with Dr. Mark Fenner - Data Scientist and Book Author of Machine Learning with Python for Everyone - and Domino Data Lab.

SPONSORDeploy Machine Learning Models with DjangoThis tutorial provides code examples on how to build your ML system with REST API. Overview of Matrix Factorization Techniques using PythonDifferent types of Matrix Factorization Techniques and Scaling mechanisms for online Recommendation Engines.The End-to-End Guide to Handling Forms in FlaskLearn to create form logic and templates in Flask with the Flask-WTForms library.My Python Development Environment, 2020 Edition Jacob Kaplan-Moss explains his Python environment setup.Detecting Natural Disasters with Keras and Deep LearningIn this tutorial, you will learn how to automatically detect natural disasters (earthquakes, floods, wildfires, cyclones/hurricanes) with up to 95% accuracy using Keras, Computer Vision, and Deep Learning.11 new Python web frameworksPick a fresh one for your next side project.The Art of Not Getting BlockedHow I used Selenium & Python to Scrape Facebook, and Tiktok.How we build Bytebase — Part One: Real-time Chat with WebSocketsThis is our first post in a series about how we build Bytebase. This series is adapted from the Django Real-Time Chat tutorial we presented at the 2019 US DjangoCon conference in San Diego, with added topics as requested.Face Detection and Recognition with KerasThis post shows how to detect faces in images using the MTCNN model in Keras and use the VGGFace2 algorithm to extract facial features and match them in different images.Contract-Driven DevelopmentSave Time by Automating SSH and SCP Tasks with Python Laziness and Streams in PythonCreating a Slack App in Python on GCP“Parsing” in PythonInteresting Projects, Tools and LibrariesstylecloudPython package + CLI to generate stylistic wordclouds, including gradients and icon shapes!MMFashionOpen-source toolbox for visual fashion analysis based on PyTorch.Fast-SRGANA Fast Deep Learning Model to Upsample Low Resolution Videos to High Resolution at 30fps.conradTrack conferences and meetups on your terminal! ScrapeGenA simple python tool that generates a requests/bs4 based web scraper.dovpandaDirections overlay for working with pandas in an analysis environment.PygameUIThis is small implementation of basic UI components to speed up building pygame projects.pwnagotchi-plugins-contrib User contributed Pwnagotchi plugins.Upcoming Events and WebinarsFour data-themed talks - Boston, MAThere will be following talks

  • D-Tale

  • Twitter sentiment analysis

  • Serving a PyTorch deep learning model on the web without a server

  • Processing data outside DataFrames with custom types

DC Python Meetup November 2019 - Arlington, VAThere will a talk, Model Serving with AWS and Lambda.Surviving without Python - Raleigh, NCPython is such a popular language for good reason: Its principles are strong. However, if Python is “the second-best language for everything”… that means the first-best is often chosen instead. Oh no! How can Pythonistas survive a project or workplace without our favorite language?PyHou Meetup November 2019 - Houston, TXThere will be a talk, Designing a Pythonic API.PyData Montreal Meetup November 2019 - Montreal, QCThere will be following talks

  • Weakly Supervised Methods for Object Detection and Localization

  • Interactive image processing with scikit-image and Dash

Interpretable Neural Networks for Text Classification - Chicago, ILThis talk will discuss the difference between global and local interpretability; what these concepts mean in the context of text classification models; and how to use two specific local interpretability methods, saliency and occlusion, to open the black box of a neural network. We will also touch on hierarchical attention networks, a neural network text classification model with built-in local interpretability in the form of attention.LjPyMeetup November 2019 - Ljubljana, Slovenia 

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