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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 ;)
The fastai book, published as Jupyter Notebooks
Python programs, usually short, of considerable difficulty, to perfect particular skills.
A collection of various deep learning architectures, models, and tips
Code for Machine Learning for Algorithmic Trading, 2nd edition.
📡 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: 🇺🇸 🇨🇳 🇯🇵 🇮🇹 🇰🇷 🇷🇺 🇧🇷 🇪🇸
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
🎨🖌 Modern, bright color theme for Sublime Text
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"