Python Weekly (Issue 463 August 27 2020)

Python Weekly - Issue 463

August 27, 2020

Python Weekly

Welcome to issue 463 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

Learn how to build an end-to-end computer vision program for building optimal peanut butter and banana sandwiches.

Most performance problems in web applications come down to one thing: the database. In this talk, Andrew Brookins helps you squeeze every ounce of database performance from your Django application. 

Python can execute code. Make sure it executes only the code you want it to.

This article will look at how maintainers of an application can manage their data through Django's built-in administrative tools. We will see how to build admin pages and customize the admin tools to help teams navigate their apps.

Let’s explore New York Times, The Guardian, HackerNews and other APIs and gather some news data for your next project!

This article demonstrates how specially crafted but ordinary gzip archives can be used as a database like storage. It also introduces a Python package and explains how it works.

In this tutorial, you will be learning how to build powerful time-series forecasting model of your own using various kinds of deep learning algorithms such as Dense Neural Networks (DNN), Convolutional Neural Network (CNN) and Recurrent Neural Networks (RNN).

In this video, you will learn how you can automate different things using Python! Starting with a Browser Based Instagram like bot, we'll work our way through to an Fortnite bot that can play the game 24/7 in idle mode.

This is a quick post about optimizing algorithms written in Python with NumPy, and implementing the same code in Rust.

An overview of common mistakes made in creating and building a Python package and how to avoid them.

How we used the power of Cython to help streamline the way that knowledge can be packaged and shared all over the world – even without the internet.

The most popular implementation of the LFU Cache Eviction Scheme, using a min-heap, implements all three operations with running time complexity of O(log n) and this makes LFU sub-optimal. In this essay, we take a detailed look at a clever algorithm that implements LFU such that all the operations happen with O(1) running time complexity.

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Interesting Projects, Tools and Libraries

A minimal PyTorch re-implementation of the OpenAI GPT (Generative Pretrained Transformer) training.

Accelerating MR Imaging with AI. A collaborative research project between Facebook AI Research (FAIR) and NYU Langone Health. The aim is to investigate the use of AI to make MRI scans up to 10 times faster.

CODAR is a framework built using PyTorch to analyze post (Text/Media) and predict if they’re involved in cyber bullying and offense.

Best Practices, code samples, and documentation for Computer Vision.

The Mys programming language - an attempt to create a statically typed Python-like language that produces fast binaries.

A terminal-based presentation tool with colors and effects.

volksdep is an open-source toolbox for deploying and accelerating PyTorch, Onnx and Tensorflow models with TensorRT.

Heuristic Vulnerable Parameter Scanner.

A minimal FastAPI implementation in Python2 + Django without pydantic.

Python module for viewing Portable Executable (PE) files in a tree-view using pefile and PyQt5. Can also be used with IDA Pro to dump in-memory PE files and reconstruct imports.

A pythonic, asynchronous, inotify interface.

A Python interface for scheduled data transfer. It facilitates transfer of (any) data from A to B, on a scheduled interval.

Python library for converting Python calculations into rendered latex.

Luminaire is a python package that provides ML driven solutions for monitoring time series data.

Upcoming Events and Webinars

A common task in processing text data is to identify named subjects such as companies and products. This is referred to as Named Entity Recognition (NER). This talk provide an overview of model-based NER and focus on an application using the spaCy NLP library on a non-English text corpus. We will discuss how to structure data for this task and considerations when evaluating results.

There will be following talks

  • Ⅹ pounds of poo in Ⅴ loud acts

  • Improving notebook collaboration and security with JupyterLab extensions

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