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- Python Weekly (Issue 502 June 3 2021)
Python Weekly (Issue 502 June 3 2021)
Python Weekly - Issue 502
Python Weekly
Welcome to issue 502 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.
News
Facebook’s AI models perform trillions of inference operations every day for the billions of people that use our technologies. Meeting this growing workload demand means we have to continually evolve our AI frameworks. Which is why, we’re announcing that we’re migrating all our AI systems to PyTorch.
In the race to build the underlying technologies that can power the next wave of AI revolution, a Chinese lab just toppled OpenAI, the venerated US-based research lab, in terms of who can train a gigantic deep learning model with the most training parameters--as for whether or not there is a race, at least ranking members of the lab believe so.
Articles, Tutorials and Talks
Learn how to use Python and machine learning to build a bioinformatics project for drug discovery.
Were you taught that function overloading isn’t possible in Python? Here’s how you can do it with generic functions and multiple dispatch!
Master Python virtual environments with conda, once and for all. Learn how to install conda from scratch, manage, and packaging virtual environments.
When removing fields from Django models, or adding non-nullable fields, it can be hard to avoid a mismatch between code running on some servers and the database in use. By using django-add-default-value and django-deprecate-fields to simplify the migration and deployment process, you will eliminate a common Django deployment headache. This has been a challenge for a while now. Believe it or not,
has been open since 2005!
Learn how to deploy a Django app to an EC2 instance using Docker Compose.
How to iterate over a pandas DataFrame is a common question, but understanding how to do it and when to avoid it are both important.
Building a live dashboard with the help of a few hardware modules.
RetroLab is an alternative JupyterLab distribution, built from the ground-up, providing a notebook interface with a retro look and feel.
This two-wheeled robot can move around a room autonomously.
Interesting Projects, Tools and Libraries
View and control remote terminals from your browser with end-to-end encryption.
ArXiv Miner is a toolkit for mining research papers on CS ArXiv.
A CUI for recovering overwritten or deleted data.
JupyterLite is a JupyterLab distribution that runs entirely in the browser built from the ground-up using JupyterLab components and extensions.
A Text User Interface with Rich as the renderer.
An easy-to-use and efficient system to support the Mixture of Experts (MoE) model for PyTorch.
PyTouch is a machine learning library for tactile touch sensing.
Automatic voice-synthetised summaries of latest research papers on arXiv.
Monty, Mongo tinified. MongoDB implemented in Python.
A Python package for geospatial analysis and interactive mapping with minimal coding in a Jupyter environment.
Libraries for applying sparsification recipes to neural networks with a few lines of code, enabling faster and smaller models.
Common financial technical indicators implemented in Pandas.
A declarative Kubeflow Management Tool inspired by Terraform.
A simple Python wrapper to AWS Dynamodb.
New Releases
Upcoming Events and Webinars
During this talk, you'll learn about ways to use printing and logging as well as pdb. You'll see how core Python tools like dbapi2 and the inspect module can help to solve common problems. You'll learn how to spot new avenues for exploration, and how to figure out when you're in a blind alley.
There will be following talks
AIQC; framework for rapid & reproducible deep learning for open science
Metabob an AI-assisted tool for debugging Python code
There will be following talks
Leveling up your Python skills as a beginner
super()...weird
We're going to put a spotlight on the coding editors we use. Jupyter Notebooks, Visual Studio Code, PyCharm, Emacs... what should you use to write your code in?
There will be following talks
Functional refactoring and Sourcery
GANs battling Covid with Sacred for ML experimentation
There will be following talks
Reproducible ML experiments (with Git & DVC)
AIQC for rapid, rigorous, & reproducible deep learning
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