Deep Learning
Core learning systems across modalities.
Deep Learning is the foundational research layer for architectures, optimization, and representation learning at Zansoc. It spans supervised, self-supervised, and reinforcement methods that feed foundation models, vision systems, and specialized industrial models.
Architectures
Transformers, hybrids, and efficient variants.
Optimization
Stable large-scale training recipes.
Representation
Self-supervised learning across sensors.
Transfer
Adaptation under limited labeled data.
Methods that survive production
We prioritize architectures and training tricks that remain stable when data is messy and compute is constrained. Reproducible recipes — not one-off leaderboard runs — are the unit of progress.
Shared instrumentation
Labs share training frameworks, eval harnesses, and cluster quotas so Vision, AI Systems, and Robotics researchers build on common deep learning primitives.
From theory to stack
Successful methods graduate into Foundation Models, Vision Systems, and product SDKs. Deep Learning technology is the research root of the vertically integrated stack.