Python Weekly (Issue 585 January 26 2023)

Python Weekly - Issue 585

January 26, 2023

Python Weekly

Welcome to issue 585 of Python Weekly. Let's get straight to the links this week.

From Our Sponsor 

Retool Mobile is the fast way for developers to build business apps for teams on the go, at a warehouse, or in the field. Teams of up to 5 can build and use unlimited mobile apps for free.

Articles, Tutorials and Talks

A course by Andrej Karpathy on building neural networks, from scratch, in code.

Asyncio provides asynchronous programming in Python with coroutines. It is exciting, new, and can be deeply frustrating to beginners. The reason is because of a series of common errors made when getting started with coroutines and the asyncio API. In this tutorial, you will discover the most common errors encountered by beginners in asyncio in Python.

In this tutorial, we will be learning about the concepts behind property-based testing, and then we will put those concepts to practice.

Learn college Algebra from an experienced university mathematics professor. You will also learn how to implement all the Algebra concepts using the Python programming language.

Last time I wrote about how Python’s1 type system and syntax is now flexible enough to represent and utilise algebraic data types ergonomically. Here, I’ll develop that idea further by way of a motivating example, for which I shall make use of some functional programming “tricks” to arrive at an efficient Python implementation.

Do you want to make software testing easier? Model-based, or stateful, testing can be a great way to save time and extra work. In this video, I'll show you how to use Hypothesis for model-based testing so that you can streamline your testing process and get better results.

This article describes techniques to accelerate a Python codebase by exposing parallelized C++ functions using PyBind. It then analyzes the results of the optimization in which parallelizing one 40-line function in a 700-line program yielded up to a 3X end-to-end speedup.

A first part of the 3 parts series that covers preparing the ground for the actual migration.

Rewriting library code to speed up my interpreter benchmark by 28%.

This post is a showcase of data wrangling techniques in Python, using glom. 

Interesting Projects, Tools and Libraries

Visually simulate Git operations in your own repos with a single terminal command.

A fast, simple and lightweight Bloom filter library for Python, fully implemented in Rust.

Next-generation IPython kernel with reactivity, execution suggestions, syntax extensions, and more. 

Super components powers for your Jinja templates.

General technology for enabling AI capabilities w/ LLMs and Generative AI models

Convert python programs into one line of python code!

Custom jupyter widgets made easy.

High Performance Machine Learning for CPUs, GPUs, Accelerators and Heterogeneous Clusters.

ChatRWKV is like ChatGPT but powered by the RWKV (100% RNN) language model, and open source.

Given an existing docker container, prints the command line necessary to run a copy of it.

Building applications with LLMs at its core through our Symbolic API leverages the power of classical and differentiable programming in Python.

Upcoming Events and Webinars

There will be following talks

  • Building Data Orchestration for Big Data Analytics in the Cloud

  • Notebooks as Functions

There will be a talk, Better and Faster NLP Model Training: 10 Proven Techniques.

There will be a talk, Data Drift: How to Uncover Changes in Your ML Model's Input Data.

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