Explore machine learning concepts related to Representation Learning. Clear explanations and practical insights.
Understanding complete, dimensional, and cluster collapse — the failure modes that every self-supervised method must prevent. Learn why collapse happens and how contrastive, asymmetric, regularization, and masking approaches solve it.
Master contrastive learning for vector embeddings: how InfoNCE loss and self-supervised techniques train models to create high-quality semantic representations.
Understand contrastive loss for representation learning: interactive demos of InfoNCE, triplet loss, and embedding space clustering with temperature tuning.
The modality gap in CLIP and vision-language models: why image and text embeddings occupy separate regions despite contrastive training.
Learning low-dimensional vector representations of graphs through random walks, DeepWalk, Node2Vec, and skip-gram models