#Deep Learning
Machine Learning Primer
September 5, 2026
Object Detection & Semantic Segmentation Primer — Reading "What Is Where" From an Image
Object Detection draws boxes around things; Semantic Segmentation colors in every pixel by meaning. From the difference between the two, through Instance and Panoptic Segmentation, the lineage from R-CNN to YOLO, DETR and DINO, all the way to their 3D point-cloud counterparts — a systematic tour of the fundamentals of scene understanding.
Fundamentals · 14 min Machine Learning Primer September 4, 2026A Thorough Guide to SegFormer — Why a Transformer Without Positional Encoding Works for Segmentation
This article breaks down SegFormer, introduced by NVIDIA, The University of Hong Kong, and collaborators at NeurIPS 2021, based on the original paper. We examine the hierarchical MiT encoder without positional encoding, the parameter-efficient All-MLP decoder, the effective receptive field, and the measured mIoU, parameter counts, and compute from B0 to B5 with equations.
Fundamentals · 9 min Machine Learning Primer September 4, 2026YOLO11 Explained in Depth — What Did C3k2 and C2PSA Change from YOLOv8?
Break down Ultralytics YOLO11 using official documentation and primary sources: C3k2, C2PSA, anchor-free detection, DFL/CIoU losses, and measured benchmarks for n/s/m/l/x.
Fundamentals · 11 min Machine Learning Primer September 4, 2026Machine Learning Primer: Neural Network Basics
Neural networks stack affine transforms and nonlinear activations, then reduce loss by backpropagation and gradient-based optimization.
Fundamentals · 5 min
Technology Trends
July 17, 2026
Technology Trends in SLAM
From filter-based methods to graph optimization, and now to learning-based, AI-native approaches. Tracing the lineage of SLAM (simultaneous self-localization and mapping) and where current research stands.
Trends · 7 min