A growing hands-on repository focused on building the foundations of machine learning, deep learning, generative AI, agentic AI, and production AI systems through implementation.
This repository is built around a simple belief:
Strong AI engineering skills compound best through building systems and understanding how intelligent systems work from first principles.
The focus is on implementing core building blocks that power modern AI systems, ranging from classical machine learning foundations to transformers, large language models, agent systems, production engineering, and deployment infrastructure.
This repository is a long-term work in progress and will evolve gradually through consistent hands-on implementation.
- Build machine learning and deep learning foundations from scratch
- Implement core building blocks behind modern AI systems
- Understand how intelligent systems work end-to-end
- Learn production engineering concepts required for deploying AI systems
- Build technical depth through implementation instead of passive learning
- Classical Machine Learning
- Neural Networks and Deep Learning
- Transformers and LLM Foundations
- Generative AI Systems
- Agentic AI and Multi-Agent Systems
- Production ML Engineering
- Inference Engineering
- Deployment and MLOps
This repository is shared publicly for anyone interested in learning AI engineering through hands-on implementation and experimentation.