An AI-powered learning project. I'm a senior software engineer (C++/Python, HPC, numerical simulation) building ML skills through hands-on projects — with an AI as teacher, not as substitute.
- 06 — Equation-to-Code (QLoRA on Qwen2-VL) — fine-tuned a Vision-Language Model (VLM) to convert images of typeset math equations into executable Python code. 91.0% validation accuracy.
- 05 — Character-Level GPT — implemented a decoder-only transformer from scratch (attention, positional encoding, autoregressive generation) following Karpathy's nanoGPT walkthrough, trained on GPU. 2.24 train / 2.27 val loss.
I have 8+ years of production C++/Python, a PhD in Computational Mechanics, and HPC/distributed computing experience. Strong applied math foundations (linear algebra, optimization, PDEs). I've done tutorial-scale and self-directed ML projects, but no professional ML experience yet. This repo is my path from numerical software engineer to ML engineer.
- I write the code. The AI provides direction, asks questions, reviews my work, and pushes back when I'm wrong.
- Framing, design, and task documents are written by the AI under my supervision.
- Projects are real and complete — each one trains a model, shows results, and has a clear README.
- Training runs on a local GPU (RTX 4070 Ti) or Kaggle/Colab free-tier GPUs.
- AI steering lives in
AGENTS.md— it sets ground rules for how the AI interacts with me (teacher, not substitute).
| # | Project | Status | Key Result |
|---|---|---|---|
| 01 | Image Classifier (CIFAR-10) | ✅ | 85% test accuracy |
| 02 | Tic-Tac-Toe (Tabular Q-Learning) | ✅ | Never loses vs random |
| 03 | Tic-Tac-Toe (DQN) | ✅ | Never loses vs random |
| 04 | CartPole (DQN) | ✅ | 500/500 perfect |
| 05 | Character-Level GPT | ✅ | 2.24 train / 2.27 val loss |
| 06 | Equation-to-Code (QLoRA Qwen2-VL) | ✅ | 91.0% validation accuracy |
| 07 | Physics-Informed AI (FEM + PINN, cantilever beam) | 🚧 | FEM solve validated vs. Euler-Bernoulli; PINN in progress |
Get fluent in PyTorch, modern training workflows, and Reinforcement Learning (RL) fundamentals.
- Image classifier (CNN on CIFAR-10) — 85% test accuracy
- Tabular Q-learning agent (Tic-Tac-Toe — Bellman equation, self-play)
- Deep Q-Network (DQN) agent (Tic-Tac-Toe — replay buffer, target network)
- DQN on CartPole (single-agent, dense reward, Gymnasium environment)
Understand attention, embeddings, and generative models.
- Character-level GPT (build a transformer from scratch, train on GPU)
- Fine-tune a small language or vision model on a custom task
- Semantic search / retrieval system
One or two deeper projects — pick based on interest and job market at the time.
- Diffusion models (image or video generation)
- World models (action-conditioned environment prediction)
- ML infrastructure (distributed training, model serving, custom CUDA kernels)
- Scientific ML (physics-informed neural networks, surrogate models)
- RL at scale (Proximal Policy Optimization (PPO) / policy gradient on a visual environment)