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Writing
Insights on Computer Vision, Deep Learning, IoT, and the intersection of AI with the real world.
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Showing 1-6 of 10 posts
Asking an AI to 'summarize this paper' is like hiring a research assistant and only asking them to make coffee. A practical guide to prompts that actually accelerate research — from literature review to hypothesis generation.
Comprehensive benchmark comparison of YOLO, SSD, and Faster R-CNN on COCO dataset — including mAP, FPS, parameters, and practical recommendations for real-time object detection in 2026.
ARIMA is not dead. In 2026, it still offers a strong baseline for Bitcoin forecasting — if you use it correctly, validate it honestly, and understand where it fails.
KNN looks simple on the surface — find the nearest neighbors, take a vote. But what actually happens mathematically? This article computes every single step: Euclidean distance, Manhattan distance, normalization, classification, and regression — digit by digit.
Everyone's obsessing over GPU clusters and cloud spend. Meanwhile, a $599 box sitting on my desk is running a 14B parameter LLM, 24/7, for roughly $52 a year in electricity. Let's talk about why Apple Silicon quietly won the local AI inference race.
The most detailed guide to understanding backpropagation — from forward pass and loss computation to weight updates, calculated digit by digit with nothing hidden.