Ranked by Attention Score: stars gained over the last 180 complete days
20 repositories by Attention Score · May 7, 2026 – Nov 2, 2026 (UTC)
The 30 Days of Python programming challenge is a step-by-step guide to learn the Python programming language in 30 days. This challenge may take more than 100 days. Follow your own pace. These videos may help too: https://www.youtube.com/channel/UC7PNRuno1rzYPb1xLa4yktw
A theoretical reconstruction of the Claude Mythos architecture, built from first principles using the available research literature.
The open source AI engineering platform for agents, LLMs, and ML models. MLflow enables teams of all sizes to debug, evaluate, monitor, and optimize production-quality AI applications while controlling costs and managing access to models and data.
Open standard for machine learning interoperability
The Open Source Feature Store for AI/ML
Build, Manage and Deploy AI/ML Systems
ZenML 🙏: One AI Platform from Pipelines to Agents. https://zenml.io.
modular quant framework.
Aim 💫 — An easy-to-use & supercharged open-source experiment tracker.
Low-code framework for building custom LLMs, neural networks, and other AI models
Open-source, low-code AutoML platform for Python. PyCaret 4.0: sklearn-native engine + React control plane.
Largest list of models for Core ML (for iOS 11+)
Superduper: End-to-end framework for building custom AI applications and agents.
Deepchecks: Tests for Continuous Validation of ML Models & Data. Deepchecks is a holistic open-source solution for all of your AI & ML validation needs, enabling to thoroughly test your data and models from research to production.
CellViT: Vision Transformers for Precise Cell Segmentation and Classification
Train and Deploy an ML REST API to predict crypto prices, in 10 steps
Multi-stage Riemannian flow matching for physically valid molecular docking, with GNINA scoring, PoseBusters filtering, CLI inference, and benchmarks.
Sharing Updatable Models (SUM) on Blockchain
A collection of state-of-the-art algorithms for the training, serving and interpretation of Decision Forest models in Keras.
ML powered analytics engine for outlier detection and root cause analysis.