python

Understanding the major differences between the Python libraries Pandas and Polars for Data Science Link

PyTorch Lightning & Lightning Hydra Template are neat. Tensor Puzzles is great intro. TorchScale is nice library for making Transformers efficiently. BackPACK is interesting too. Link

PyTorch deep learning project made easy. Link

A Guide to Isolating and Managing your Machine Learning Models in a Django Framework Link

A crash course on using Beam to run an entire ML workflow, from development to deployment. Link

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Sketch is an AI code-writing assistant for pandas users that understands the context of your data, greatly improving the relevance of suggestions. Sketch is usable in seconds and doesn’t require adding a plugin to your IDE. Link

Have you ever wondered where your lengthy processing was, and when it would finish? Ever found yourself hitting [RETURN] now and then to ensure it didn’t hang, or if, in a remote SSH session, the connection was still working? Ever needed to pause some processing for a while, return to the Python prompt for a manual inspection or fixing an item, and then resume the process seamlessly? Link

The Rich API makes it easy to add color and style to terminal output. Rich can also render pretty tables, progress bars, markdown, syntax highlighted source code, tracebacks, and more — out of the box. Link

Learning representations of algorithms is an emerging area of machine learning, seeking to bridge concepts from neural networks with classical algorithms. The CLRS Algorithmic Reasoning Benchmark (CLRS) consolidates and extends previous work toward evaluation algorithmic reasoning by providing a suite of implementations of classical algorithms. These algorithms have been selected from the third edition of the standard Introduction to Algorithms by Cormen, Leiserson, Rivest and Stein. Link

We are excited to present this year’s picks for the most innovative developments in the Python ecosystem. From this edition, we are expanding our list to include not only libraries per-se, but also tools that are built to belong in the Python ecosystem — some of which are not written in Python as you’ll see. Link

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The facets project contains two visualizations for understanding and analyzing machine learning datasets: Facets Overview and Facets Dive. Link

This post is related to the recent release of a new open-source project called OpenMetricLearning (OML), and one of its goals is to lower the entry threshold for metric learning pipelines. We will briefly introduce the theory, discuss the examples in code and show how simple heuristics can perform on a level comparable with the current SotA. Since the project is new, each star on GitHub is essential for us.

Time series forecasting is an essential scientific and business problem and as such has also seen a lot of innovation recently with the use of deep learning based models in addition to the classical methods. An important difference between classical methods like ARIMA and novel deep learning methods is the following. Link