Highlights: We implement a complete DDPM from scratch on 1D sine waves — same math as image diffusion, but every intermediate state is plottable. We track 100 parallel trajectories, measure when the model “commits” to a specific sample, then design a controlled experiment that reveals manifold compactness as the key factor determining whether diffusion succeeds or fails. So let’s begin! Tutorial Overview: Why 1D? The Dataset Forward Process Model and Training Generating from Noise What…
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Highlights: In this post, we take a deep dive into the architecture that changed everything — the Transformer — and trace its evolution from NLP into computer vision. We start with the original encoder-decoder model, walk through self-attention and multi-head attention step by step, and then show how Vision Transformers (ViT) apply the exact same mechanism to image patches instead of words. Along the way, we answer the questions that trip everyone up: if we…
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  Highlights: In this post, you’ll learn how CLIP connects images and text in a shared embedding space — enabling zero-shot image classification, semantic search, and visual perception scoring without any task-specific training. We start from the ground up with Vision Transformers, walk through CLIP’s contrastive learning architecture, run hands-on embedding experiments, and then push CLIP to its limits with a real-world challenge: can it tell cheap bedrooms from expensive ones using actual house sale…
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