QuarkML is a technical publication focused on understanding machine learning systems from first principles. We are interested in work that examines how modern learning systems are constructed, how they operate internally, and why they behave the way they do.
If you care about the foundations of machine learning, mathematics, representations, optimization, architectures, and training dynamics, and you enjoy thinking in terms of systems rather than tools, QuarkML may be a good fit for your work.
Writing for QuarkML is an opportunity to:
Develop and formalize your own understanding
Document experiments, architectures, and technical explorations
Share rigorous explanations and implementations with a technically serious audience
We welcome articles such as:
Deep dives into core concepts in machine learning and deep learning
Architectural breakdowns and system-level analyses
First-principles implementations and technical walkthroughs
Open-ended experiments, research notes, and exploratory studies
The emphasis is on clarity, depth, and original reasoning. Content should aim to explain not only what works, but why it works.
If this style of work resonates with you, feel free to reach out.
Contact: info@quarkml.com