#Attention

Articles about Attention — exploring patterns, best practices, and real-world implementations in production systems.

3 posts tagged with attention. ← All posts

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Pratik Dhanave · ·5 min read

Efficient Attention

The two costs of long context — quadratic attention compute and linear KV-cache memory — each have a family of solutions, and together they're why modern models can handle context lengths that were impossible a few years ago. Grouped-query attention shrinks the KV cache; FlashAttention computes exact attention far faster; sliding-window and sparse patterns break the quadratic. This post covers the techniques that made long context practical.

The two costs of long context each have a family of solutions, and together they're why modern models handle context lengths that were impossible a few years ago. Grouped-query attention shrinks the KV cache; FlashAttention computes exact attention far faster; sliding-window and sparse patterns break the quadratic. The techniques that made long context practical, and which cost each attacks.

#LLMs#Attention#AI Architecture#Performance#Machine Learning
Pratik Dhanave · ·6 min read

Graph Attention Networks — Learning Which Neighbors Matter

The graph convolutional network treats all of a node's neighbors as (near) equals. But that's often wrong — some neighbors are far more relevant than others, and which ones depends on the context. Graph attention networks fix this by letting the model learn how much to weigh each neighbor, bringing the attention mechanism that revolutionized language models to graphs. This post explains attention on graphs and why it's so often the right upgrade over plain averaging.

The GCN treats all neighbors as equal, but some matter far more and which ones is context-dependent. Graph attention networks learn data-dependent weights so a node listens more to important neighbors — the transformer's attention mechanism adapted to graphs — with interpretability as a bonus. When it helps and the honest trade-offs.

#Graph Neural Networks#Attention#GAT#Deep Learning#Machine Learning
Pratik Dhanave · ·6 min read

Vision Transformers — When Attention Came for Images

For a decade, convolution was synonymous with computer vision. Then the transformer — the architecture that revolutionized language — was applied to images, and the vision transformer showed that attention could match or beat CNNs given enough data. It's a profound shift: the same architecture now underpins both language and vision, enabling the multimodal models that see and read at once. This post explains how transformers handle images and what their rise means.

The vision transformer treats an image as a sequence of patches and runs a standard transformer, giving global context immediately. The deep trade-off (CNN inductive bias vs ViT learning from data — ViTs win at scale) and the big consequence: one architecture now unifies vision and language, enabling multimodal AI.

#Computer Vision#Vision Transformer#Attention#Multimodal AI#Deep Learning#Machine Learning

All posts on this site are written by Pratik Dhanave, an Agentic AI Architect with 7+ years building production distributed systems, multi-agent AI platforms, and cloud-native infrastructure. About the author → Each article includes working code, architecture diagrams, and references to the specific frameworks and standards discussed. Browse all posts or explore related topics using the tag cloud above.