Why Embedding Dimensions Matter More Than Layer Count
Model depth gets the headlines, but embedding width determines what your network can actually represent.
Why Cross-Attention Enables Powerful Multimodal Models
The architectural pattern that lets AI systems see, read, and reason across different data types simultaneously
How Quantization Shrinks Models Without Destroying Performance
Neural networks waste precision everywhere. Quantization recovers what matters and discards what never did.
The Geometry of Softmax Attention Bottlenecks
Why attention scores collapse to few tokens as sequences grow, and what we sacrifice to fix it
The Surprising Power of Simple Tokenization Choices
How text segmentation algorithms create invisible constraints on model capacity, efficiency, and linguistic fairness
How Speculative Decoding Accelerates Text Generation
The draft-and-verify paradigm that makes large language models respond faster without changing a single output token
How KV Caching Makes Autoregressive Generation Practical
Understanding the memory-compute trade-off that transforms quadratic attention costs into practical real-time text generation
How Dropout Actually Provides Regularization
Understanding why randomly breaking your network during training creates robust, generalizable neural representations
Why Positional Encodings Are More Important Than You Think
The hidden architectural choice that determines whether your transformer understands sequences or just sees token soup
Why Layer Normalization Beats Batch Normalization for Transformers
Understanding why transformers abandoned batch statistics reveals fundamental principles for designing stable, deployable neural network architectures.
The Critical Role of Initialization in Deep Network Training
Master the mathematics of weight initialization to ensure your deep networks can actually learn from their first gradient update.
The Architecture Behind Flash Attention's Speed Gains
How restructuring memory access patterns unlocks dramatic speedups in transformer attention without changing the mathematics
Why Transformer Layers Learn Hierarchical Representations
Discover how stacked transformer layers spontaneously organize into hierarchies of meaning—from surface tokens to abstract reasoning
The Real Cost of Model Parameters You're Not Measuring
Parameter count misleads—master the memory bandwidth, activations, and deployment constraints that actually determine your AI system's real-world cost.