Machine Learning · GitHub

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A curated list of MLOps references

A Cloud Native Batch System (Project under CNCF)

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A Unified Face Analysis Library for Python | Detection, alignment, landmarks, face-mesh, recognition, parsing, gaze, attributes and anti-spoofing under one API.

List of articles related to deep learning applied to music

a machine learning image inpainting task that instinctively removes watermarks from image indistinguishable from the ground truth image

:fire: 2D and 3D Face alignment library build using pytorch

A high-fidelity 3D face reconstruction library from monocular RGB image(s)

• Reconstructs face at high fidelity from single or multiple RGB images
• Estimates face geometry

Python library for Representation Learning on Knowledge Graphs https://docs.ampligraph.org

• Intuitive APIs: AmpliGraph APIs are designed to reduce the code amount required to learn models that predict links in knowledge graphs. The new version AmpliGraph 2 APIs are in Keras style, making the user experience even smoother.
• GPU-Ready: AmpliGraph 2 is based on TensorFlow 2, and it is designed to run seamlessly on CPU and GPU devices - to speed-up training.

Official implementation of the RAVE model: a Realtime Audio Variational autoEncoder

SuperSLAM: Open Source Framework for Deep Learning based Visual SLAM

Visual Novel Character Creation Suite is a comprehensive tool for creating character sprites for visual novels. It allows you to create unique characters with a consistent appearance across all images, which was previously a challenging task when using neural networks.

Source code of PyGAD, a Python 3 library for building the genetic algorithm and training machine learning algorithms (Keras & PyTorch).

Master AI inference, AI agent harness systems, and hardware engineering — then design a physical AI chip. That is the goal.

The collection of pre-trained, state-of-the-art AI models for ailia SDK

Complete SOC Analyst Training Roadmap 2026: 14 hands-on projects (SIEM, SOAR, TIP, ML) for automation-first defenders

Daily notes on AI research, especially those on LLMs, VLMs, AI agents and related techniques such as pre-, mid-, post-training, RL, continual learning, test-time scaling, RSI, and more.

Notes & exercise solutions of Part I from the book: "Hands-On ML with Scikit-Learn, Keras & TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems" by Aurelien Geron