Python Weekly (Issue 361 - August 23 2018)

Python Weekly - Issue 361Â

August 23, 2018

Python Weekly

Welcome to issue 361 of Python Weekly. Let's get straight to the links this week.

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Articles, Tutorials and Talks

In this introductory tutorial, we'll look at what Python decorators are and how to create and use them.

Notebooks have rapidly grown in popularity among data scientists to become the de facto standard for quick prototyping and exploratory analysis. At Netflix, we’re pushing the boundaries even further, reimagining what a notebook can be, who can use it, and what they can do with it. And we’re making big investments to help make this vision a reality. In this post, we’ll share our motivations and why we find Jupyter notebooks so compelling. We’ll also introduce components of our notebook infrastructure and explore some of the novel ways we’re using notebooks at Netflix.

In this tutorial you will learn how to use OpenCV to detect text in natural scene images using the EAST text detector.

This is a practical, end-to-end guide on how to build a mobile application using TensorFlow Lite that classifies images from a dataset for your projects.

Or, “things you should never ever do in production”

In this tutorial you'll learn how to work effectively with Python’s set data type. You'll see how to define set objects in Python and discover the operations that they support and by the end of the tutorial you'll have a good feel for when a set is an appropriate choice in your own programs.

How to build an ASGI web server, like Hypercorn.

One of the many benefits of transfer learning is that you don’t need to provide as much of your own training data as you would if you were starting from scratch. But where do these pre-existing models come from? That’s where TensorFlow Hub comes in handy: it provides a whole repository of existing model checkpoints for various types of models — images, text, and more. In this post, I’ll walk you through building a model to predict the genre of a movie from its description using a TensorFlow Hub text module.

DistBelief is a Google paper that describes how to train models in a distributed fashion. In particular, we were interested in implementing a distributed optimization method, DownpourSGD. This is an overview of our implementation, along with some problems we faced along our way.

Solving a complete machine learning problem for societal benefit.

  • Part Two - Getting the most from our model, figuring out what it all means, and experimenting with new techniques.

This Python PostgreSQL tutorial demonstrates how to develop Python database applications with the PostgreSQL database server.

In the previous section, I covered some basic time series analysis on the Zillow Rental Market dataset for the Bay Area. The analysis is performed in Python using Jupyter, Pandas, and Plotly. In this second part of the series we take a look at some public Redfin data. Specifically taking a deeper dive into purchasing a property in the East Bay.

Warning: You probably don't want to do this because it's unsupported and very hacky. Consider JSONField.

Interesting Projects, Tools and Libraries

Pytorch implementation of our method for high-resolution (e.g. 2048x1024) photorealistic video-to-video translation.

Papermill is a tool for parameterizing, executing, and analyzing Jupyter Notebooks.

Python-based extendable tool.

Provides In-memory compilation and reflective loading of C# apps for AV evasion.

Telegram bot that forwards messages to your inbox. Useful for GTD and email geeks.

Simple and flexible progress bar for Jupyter Notebook and console.

pychromecastweb is a web application that allows you to browse parts of the local filesystem and cast videos to a Chromecast.

Convert an image into ASCII Art.

Handle many API calls from a single HTTP request.

Hunt for security weaknesses in Kubernetes clusters.

An example of Django project with basic user functionality.

Upcoming Events and Webinars

There will be following talks

  • Why should I learn Pandas?

  • Writing for Engineers

There will be following talks

  • Squashing Django Migrations

  • Django on App Engine and the Datastore

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