Comprehensive algorithmic derivations, linear algebra formulations, and architectural deep dives bridging mathematical theory with our interactive simulation workbenches.
A comprehensive derivation of scaled dot product attention, projection matrices, softmax normalization, and key value cache memory scaling in autoregressive transformers.
How frontier multimodal AI models discretize non text media, examining Vision Transformer patch projections, OpenAI tile decomposition, and Residual Vector Quantization audio codecs.
A mathematical deep dive into 2D discrete convolutions, Frobenius inner products, kernel weights, spatial feature maps, and pooling operations in deep vision networks.
A comprehensive derivation of scaled dot product attention, projection matrices, softmax normalization, and key value cache memory scaling in autoregressive transformers.
An in depth exploration of how subword tokenization transforms raw text into discrete integers, covering frequency statistics, byte level fallbacks, and vocabulary compression.
Every technical concept in our guides can be explored through client side simulations with live WebAssembly computation and real time 3D graphics.
Inspect subword merges, token IDs, and vocabulary boundaries in real time.
WebGL tensor flows, attention heads, and layer normalization in 3D space.
2D discrete matrix kernels, Sobel edge filters, and sliding windows on MNIST.
Analyze ViT patch embeddings and 512x512 tile decompositions.
