Hyperparameter Tuning Tips that 99% of Data Scientists Overlook
This video shows how to tune XGBoost models with Optuna while maximizing speed using XGBoost 3.0’s GPU acceleration for 5–15x faster training. He explains why cross-validation is crucial, recommends smart tuning practices, and demonstrates how Optuna’s visualizations help identify impactful hyperparameters in real-world tabular data workflows.
https://www.youtube.com/watch?v=D9xPjkOwpNk
This video shows how to tune XGBoost models with Optuna while maximizing speed using XGBoost 3.0’s GPU acceleration for 5–15x faster training. He explains why cross-validation is crucial, recommends smart tuning practices, and demonstrates how Optuna’s visualizations help identify impactful hyperparameters in real-world tabular data workflows.
https://www.youtube.com/watch?v=D9xPjkOwpNk
YouTube
Hyperparameter Tuning Tips that 99% of Data Scientists Overlook
In this video you will learn about hyperparameter tuning for XGBoost models using optuna. We also will leverage XGBoost 3.0's GPU support for 5-15x speedup (no code changes required) while training the models.
Kaggle notebook here: https://bit.ly/RobMul…
Kaggle notebook here: https://bit.ly/RobMul…
MathFlow
Likerequestsfor mathematical computing, making complex math feel simple.
https://github.com/cybergeek1943/MathFlow
Likerequestsfor mathematical computing, making complex math feel simple.
https://github.com/cybergeek1943/MathFlow
GitHub
GitHub - cybergeek1943/MathFlow: Like `requests` for mathematical computing, making complex math feel simple.
Like `requests` for mathematical computing, making complex math feel simple. - cybergeek1943/MathFlow
Today I learned that Python doesn't care about how many spaces you indent as long as it's consistent
https://www.reddit.com/r/Python/comments/1nkidxq/today_i_learned_that_python_doesnt_care_about_how/
https://www.reddit.com/r/Python/comments/1nkidxq/today_i_learned_that_python_doesnt_care_about_how/
Reddit
From the Python community on Reddit
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numethods
A lightweight, from-scratch, object-oriented Python package implementing classic numerical methods.
https://github.com/denizd1/numethods
A lightweight, from-scratch, object-oriented Python package implementing classic numerical methods.
https://github.com/denizd1/numethods
GitHub
GitHub - denizd1/numethods: A lightweight, from-scratch, object-oriented Python package implementing classic numerical methods.
A lightweight, from-scratch, object-oriented Python package implementing classic numerical methods. - denizd1/numethods
noScribe
Cutting edge AI technology for automated audio transcription. A nice GUI for OpenAIs Whisper and pyannote (speaker identification).
https://github.com/kaixxx/noScribe
Cutting edge AI technology for automated audio transcription. A nice GUI for OpenAIs Whisper and pyannote (speaker identification).
https://github.com/kaixxx/noScribe
GitHub
GitHub - kaixxx/noScribe: Cutting edge AI technology for automated audio transcription. A nice GUI for OpenAIs Whisper and pyannote…
Cutting edge AI technology for automated audio transcription. A nice GUI for OpenAIs Whisper and pyannote (speaker identification) - kaixxx/noScribe
Django: Introducing django-watchfiles, for more efficient runserver autoreloading
Django Watchfiles is a library that improves Django's development server by replacing the default autoreloader with the faster, more reliable watchfiles backend. It simplifies setup, enhances reload speed, and brings better cross-platform support with minimal configuration for Django projects.
https://adamj.eu/tech/2025/09/22/introducing-django-watchfiles/
Django Watchfiles is a library that improves Django's development server by replacing the default autoreloader with the faster, more reliable watchfiles backend. It simplifies setup, enhances reload speed, and brings better cross-platform support with minimal configuration for Django projects.
https://adamj.eu/tech/2025/09/22/introducing-django-watchfiles/
adamj.eu
Django: Introducing django-watchfiles, for more efficient runserver autoreloading - Adam Johnson
Django’s runserver automatically reloads when you change Python files. Without this autoreloading feature, you’d need to manually restart the server every time you made a code change.
RamTorch
A PyTorch library for memory-efficient deep learning that enables training and inference of large models that don't fit in GPU memory.
https://github.com/lodestone-rock/RamTorch
A PyTorch library for memory-efficient deep learning that enables training and inference of large models that don't fit in GPU memory.
https://github.com/lodestone-rock/RamTorch
GitHub
GitHub - lodestone-rock/RamTorch: RAM is all you need
RAM is all you need. Contribute to lodestone-rock/RamTorch development by creating an account on GitHub.
PEP 806 – Mixed sync/async context managers with precise async marking
https://www.reddit.com/r/Python/comments/1nqnm44/pep_806_mixed_syncasync_context_managers_with/
https://www.reddit.com/r/Python/comments/1nqnm44/pep_806_mixed_syncasync_context_managers_with/
Reddit
From the Python community on Reddit: PEP 806 – Mixed sync/async context managers with precise async marking
Explore this post and more from the Python community