Highlights: Can you score color quality algorithmically? Not as taste — as math. This post builds a scoring system from first principles: two adjacent color squares, then triplets, then a real room with three spatial regions. We walk through every formula with brand and flag examples you already know, then prove that geometry alone can move the score by five points on an identical palette. Four pair scoring dimensions: contrast (WCAG luminance), harmony (hue peaks),…
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Highlights: Every computer vision pipeline that touches color starts with the same mistake: using RGB. RGB is built for screens, not for human perception. In this post we build a complete color scoring system for living room images — from the right color space (Lab), through palette extraction (K-means), to a two-color harmony scorer tested on 10 global brand palettes. We discover why luxury brands deliberately score low, and what that means for your model.…
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Highlights: Every modern LLM agent — from ChatGPT plugins to Claude tools — relies on a single learned skill: outputting a structured JSON function call instead of free text. In this post we teach that skill to a 3-billion parameter model using QLoRA on a free Google Colab T4. We start from the fundamentals — why fine-tuning, when LoRA, how quantization works — then build the full training pipeline from scratch. By the end, your…
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Highlights: Stable Diffusion doesn’t paint an image in one shot — it sculpts one from static, guided by your words. In this post we disassemble the entire machine. We start with the VAE that compresses pixels into a tractable latent space, walk through the forward and reverse diffusion processes, open up the UNet to see how cross-attention physically connects text tokens to spatial regions, and finish with the complete Latent Diffusion architecture diagram that ties…
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Highlights: Every time you use Stable Diffusion, DALL-E, or Sora, the model never touches a single pixel during its main computation. It works entirely inside a compressed, structured space of floating-point numbers — a latent space learned by a VAE. In this post we build that space from scratch. We start from the simplest possible compression — an AutoEncoder on MNIST digits — understand why it fails at generation, fix it with the VAE’s probabilistic…
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Highlights: In this post we build a complete understanding of diffusion models from the ground up — what they are, how images are represented, how the network is trained, what it geometrically learns, and finally how we turn that geometry into samples using DDIM and DDPM. Every formula is accompanied by concrete numbers you can verify by hand. So let’s begin! Tutorial Overview: What Are Diffusion Models? How Images Are Represented The Denoiser Network Noise…
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In this post we build a complete diffusion model from scratch — training a UNet on a custom dataset, implementing the full DDPM pipeline, and understanding the math that makes iterative denoising work. We cover noise schedules, the reparameterization trick, FID evaluation, and three diffusion objectives (ε, x₀, v). By the end you’ll have generated novel images from pure Gaussian noise, and understand why diffusion models overtook GANs as the dominant paradigm for image generation.…
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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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Highlights: In this guide, you’ll learn how to pretrain a large language model from scratch — implementing training loops, evaluation metrics, and advanced text generation strategies. We’ll build a complete GPT-style training pipeline, watch it evolve from random gibberish to coherent text, and explore techniques like temperature scaling and top-k sampling. By the end, you’ll load professional pretrained weights into your own architecture. Source: This is part of our ongoing “Building LLMs from Scratch” series…
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Highlights: In this post, we build a complete GPT-2 model (124 million parameters) from scratch in PyTorch. We implement every component — layer normalization, GELU activations, the feed forward network, shortcut connections — and wire them into a transformer block that we stack 12 times to create the full architecture. By the end, you will have a structurally complete GPT model that can generate text token by token. We also weave in key insights from…
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