Stars
The ultimate open-source RAG (Retrieval-Augmented Generation) system. TRUST framework for enterprise AI, production-grade demos, and end-to-end retrieval, evaluation, and governance—all in one repo.
A comprehensive catalog of 34 Retrieval-Augmented Generation (RAG) techniques. Features Python pseudocode, pros/cons, and architecture concepts ranging from basic retrieval to advanced agentic RAG,…
Learn how to develop, deploy and iterate on production-grade ML applications.
A series of Jupyter notebooks that walk you through the fundamentals of Machine Learning and Deep Learning in Python using Scikit-Learn, PyTorch, and Hugging Face libraries.
My journey and lecture from Super AI Engineer Season 5 by AIAT
ML-powered manga translator, written in Rust.
A Library for Advanced Deep Time Series Models for General Time Series Analysis.
SCB Co-Operative Internship 2026 - Machine Learning Engineer Take-home exam: Fraud Detection with XGBoost
8-week curriculum for AI Builders
Become a cracked AI/ML Research Engineer
Contains Solutions and Notes for the Machine Learning Specialization By Stanford University and Deeplearning.ai - Coursera (2022) by Prof. Andrew NG
Beginner Level Deep Learning Tutorials in Pytorch with Youtube Videos!
A complete daily plan for studying to become a machine learning engineer.
PKBoost: Adaptive GBDT for Concept Drift, Built from scratch in Rust, PKBoost manages changing data distributions in fraud detection with a fraud rate of 0.2%. It shows less than 2% degradation und…
Learning material and codes for my Chulalongkorn courses
Ultimate AI research and engineering course
Tensors and Dynamic neural networks in Python with strong GPU acceleration
machine learning and deep learning tutorials, articles and other resources
Implement a reasoning LLM in PyTorch from scratch, step by step
A topic-centric list of HQ open datasets.
This repository covers Implemented projects
R1-Code-Interpreter: Training LLMs to Reason with Code via Supervised and Reinforcement Learning
12 Weeks, 24 Lessons, AI for All!
A Convolutional Neural Network implemented from scratch (using only numpy) in Python.