Mario Gemoll

An end-to-end robotics project covering inverse kinematics, motion planning, computer vision, sim-to-real transfer, ACT and diffusion/flow policies.

A guide to LLM posttraining basics: LoRA, SFT, RLHF, RLVR.

Foundations of reinforcement learning, from Markov decision processes and value functions to dynamic programming, Monte Carlo methods, temporal-difference learning, and policy gradients.

Extending the flow matching framework to diffusion models.

An overview of flow matching techniques for generative modeling.

Describing the training of an encoder-decoder transformer, following the "Attention Is All You Need" paper.

A viewer for the datasets used to train the model for the Attention Is All You Need page.

A page exploring the basics of normalizing flows.

An interactive exploration of Variational Autoencoders (VAEs) with theory, implementation details, and live demonstrations using a synthetic dataset.