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Python Weekly

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

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Articles, Tutorials and Talks

Neural Networks: Zero to Hero

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

5 Common Asyncio Errors in Python (and how to avoid them)
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.

Getting Started With Property-Based Testing in Python With Hypothesis and Pytest
In this tutorial, we will be learning about the concepts behind property-based testing, and then we will put those concepts to practice.

College Algebra – Full Course with Python Code

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.

Functional Python, Part II: Dial M for Monoid
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.

How to Use Hypothesis for Model-based Testing (Step by Step)

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.

Python Bindings for Performance Optimization: A Zero to One Guide
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.

Migrating from Flask to FastAPI, Part 1
A first part of the 3 parts series that covers preparing the ground for the actual migration.

Profiling and Optimizing an Interpreter
Rewriting library code to speed up my interpreter benchmark by 28%.

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

Thoughts on the Python packaging ecosystem

Hello, PNG!

Sizing Up Neural Nets

Testing a Python project using the WASI build of CPython with pytest

Interesting Projects, Tools and Libraries

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

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

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

JinjaX
Super components powers for your Jinja templates.

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

Flatliner
Convert python programs into one line of python code!

anywidget
Custom jupyter widgets made easy.

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

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

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

SymbolicAI
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

PyData Seattle Meetup January 2023
There will be following talks

Building Data Orchestration for Big Data Analytics in the Cloud
Notebooks as Functions

Virtual: PyData Munich Meetup February 2023
There will be a talk, Better and Faster NLP Model Training: 10 Proven Techniques.

PyData Edinburgh Meetup February 2023
There will be a talk, Data Drift: How to Uncover Changes in Your ML Model's Input Data.

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Python Weekly - Python Weekly - Issue 585

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