Empowering developers and organizations with comprehensive open-source learning resources to master AI technologies.
Dedicated to democratizing AI education through comprehensive, open-source learning resources.
AIComputing101 is committed to making AI education accessible to everyone. We provide comprehensive, hands-on learning resources that empower developers, students, and organizations to master AI technologies—from fundamentals to advanced implementations.
In-depth tutorials and examples covering the full spectrum of AI technologies.
All our projects are freely available on GitHub for the global community.
Production-ready code examples with best practices and optimization techniques.
Comprehensive 101 projects offering hands-on learning from fundamentals to production-ready implementations.
From quantum mechanics basics to implementing algorithms on real quantum simulators — with both experimental and optimized versions.
View on GitHubMaster parallel computing with CUDA and ROCm — featuring both educational examples and performance-optimized production code.
View on GitHubLearn RL from tabular methods to deep reinforcement learning — with POCs for understanding and production-ready agents.
View on GitHubDeep dive into ML frameworks like TensorFlow, PyTorch, and JAX — from basics to custom model architectures and optimization techniques.
Coming SoonExplore LLM fundamentals, fine-tuning techniques, and deployment strategies — from basic transformers to production-scale inference.
Coming SoonMaster distributed LLM training and inference at scale — covering cluster setup, orchestration, and performance optimization.
Coming SoonBuild intelligent autonomous agents — from basic task automation to complex multi-agent systems with reasoning capabilities.
Coming SoonLearn compiler design from lexical analysis to code generation — build your own language compiler and understand optimization techniques.
Coming SoonExplore computer architecture from digital logic to modern processors — covering ISA design, pipelining, caching, and hardware-software interface.
Coming SoonTechnical insights, project updates, and industry perspectives on AI innovation.
From your "return 42" POC to GPU compilers and beyond, the journey is defined by abstraction (IR layers) and hardware synergy.
Read moreMaster practical, hands-on approach while providing a more comprehensive view of GPU optimization strategies applicable across the major hardware ecosystems.
Read moreExplores Qiskit's architecture, circuit design principles, simulation capabilities, and the critical role of hybrid quantum-classical computing in addressing current quantum technology limitations.
Read moreAIComputing101 thrives on collaboration. Join developers, researchers, and AI enthusiasts worldwide in building the future of AI education.
Contribute implementations, optimizations, and new examples to our 101 repositories.
Help us improve documentation, tutorials, and learning resources for the community.
Share your knowledge and help others learn by answering questions and reviewing PRs.
Whether you're a beginner or an expert, there's a place for you in our community. Every contribution, no matter how small, helps make AI education more accessible.
Join our community of learners and start mastering AI technologies through our comprehensive, open-source learning resources.