Python Weekly (Issue 408 August 1 2019)

Python Weekly - Issue 408

August 01, 2019

Python Weekly

Welcome to issue 408 of Python Weekly. Let's get straight to the links this week.

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News

We are pleased to announce the availability of the new PyPy for AArch64. This port brings PyPy's high-performance just-in-time compiler to the AArch64 platform, also known as 64-bit ARM. With the addition of AArch64, PyPy now supports a total of 6 architectures: x86 (32 & 64bit), ARM (32 & 64bit), PPC64, and s390x. 

Articles, Tutorials and Talks

Learn how to build an app like SmartSneaks that lets a user convert a song or image into a generated shoe design! This is an example of how AI can be used to transform retail by giving users a more personalized experience. The tools used to build this are the Flutter framework for mobile development and the Flask framework for web development. There are 3 learning objectives in this video including how to build a deep learning API for your mobile app, how to generate images with a generative adversarial network, and how to calculate image similarity with OpenCV.

Searching for pulsars is a labor-intensive process that requires experienced astronomers and trained volunteers for their classification. In this article, we implement machine learning techniques to facilitate the process.

How to profile and optimize Python apps.

Best practices for setting up a project to maintain the highest quality code.

In this tutorial, you will learn how to use Cyclical Learning Rates (CLR) and Keras to train your own neural networks. Using Cyclical Learning Rates you can dramatically reduce the number of experiments required to tune and find an optimal learning rate for your model.

This post shows you how to correctly configure your Django project to allow you to upload files safely and securely to an AWS S3 bucket. This is a great way of handling files in Django and as you will see in this guide, is quick and easy to set up.

With full Python code to scrape, extract, transform and load it into a HDF5 data store to please your future self.

A two-part tutorial on how to create an Amazon price tracker.

Grid studio is a web-based spreadsheet application with full integration of the Python programming language. It intenteds to provide an integrated workflow for loading, cleaning, manipulating, and visualizing data. This is achieved through a spreadsheet backend written in Go with integration of the Python runtime to manipulate its contents.

In the first part, we covered 4 main important processes that are part of the automation process. In this second and final part, we will bring everything together and build our report automation system.

In this guide, we will demonstrate how to install and configure some components on Debian 10 to support and serve Django applications. We will be setting up a PostgreSQL database instead of using the default SQLite database. We will configure the Gunicorn application server to interface with our applications. We will then set up Nginx to reverse proxy to Gunicorn, giving us access to its security and performance features to serve our apps.

This article introduces how to build a Python and Flask based web application for performing text analytics on internet resources such as blog pages. To perform text analytics I will utilizing Requests for fetching web pages, BeautifulSoup for parsing html and extracting the viewable text and, apply the TextBlob package to calculate a few sentiment scores.

This article is an end to end tutorial of automating google calendar with python.

Interesting Projects, Tools and Libraries

Simple command line forensics tool for tracking USB device artifacts (history of USB events) on GNU/Linux.

Lahja is a generic multi process event bus implementation written in Python 3.6+ to enable lightweight inter-process communication, based on non-blocking asyncio.

GNES is Generic Neural Elastic Search, a cloud-native semantic search system based on deep neural network. 

A next generation HTTP client for Python.

Automated System Hardening Framework.

A tool for extracting plain text from Wikipedia dumps.

An easy to use open-source library for advanced Deep Learning and Natural Language Processing.

New Releases

The 1.17.0 release contains a number of new features that should substantially improve its performance and usefulness. The Python versions supported are 3.5-3.7, note that Python 2.7 has been dropped.

Upcoming Events and Webinars

PyCon MY 2019 is an annual gathering of Malaysian Python enthusiast.

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