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Python Data Science Handbook: full text in Jupyter Notebooks
Learn how to design, develop, deploy and iterate on production-grade ML applications.
aka "Bayesian Methods for Hackers": An introduction to Bayesian methods + probabilistic programming with a computation/understanding-first, mathematics-second point of view. All in pure Python ;)
Python programs, usually short, of considerable difficulty, to perfect particular skills.
The fastai book, published as Jupyter Notebooks
A collection of various deep learning architectures, models, and tips
📡 Simple and ready-to-use tutorials for TensorFlow
A hands-on introduction to video technology: image, video, codec (av1, vp9, h265) and more (ffmpeg encoding). Translations: 🇺🇸 🇨🇳 🇯🇵 🇮🇹 🇰🇷 🇷🇺 🇧🇷 🇪🇸
Code for Machine Learning for Algorithmic Trading, 2nd edition.
A multi-voice TTS system trained with an emphasis on quality
State-of-the-Art Deep Learning scripts organized by models - easy to train and deploy with reproducible accuracy and performance on enterprise-grade infrastructure.
🤖 💬 Deep learning for Text to Speech (Discussion forum: https://discourse.mozilla.org/c/tts)
Best Practices, code samples, and documentation for Computer Vision.
Code samples used on cloud.google.com
Evidently is an open-source ML and LLM observability framework. Evaluate, test, and monitor any AI-powered system or data pipeline. From tabular data to Gen AI. 100+ metrics.
A scikit-learn compatible neural network library that wraps PyTorch
A collection of infrastructure and tools for research in neural network interpretability.
A better notebook for Scala (and more)
Python code for common Machine Learning Algorithms
felixge's notes on the various go profiling methods that are available.
An easy to use blogging platform, with enhanced support for Jupyter Notebooks.
Python-based tools for document analysis and OCR
Fast and flexible AutoML with learning guarantees.
Repository for "Introduction to Artificial Neural Networks and Deep Learning: A Practical Guide with Applications in Python"