365-Days-Computer-Vision-Learning-Linkedin-Post

Why we included this project

One architecture per day for a full year, each paired with a short LinkedIn post that explains it, makes this a practical way to build a mental map of computer vision. The ordering matters: it starts with detection models like EfficientDet and YOLO, moves through segmentation networks such as U-Net and DeepLab, then covers transformers, super-resolution, and medical imaging variants. Every entry links to a concise explainer, so you can absorb one concept a day without digging through the original papers. It works as a self-paced syllabus, a checklist for gaps in your own knowledge, or a quick reference when you need a one-paragraph summary before reading the full paper. Just keep in mind this is a curated index of explainers, not software you deploy, so treat it as a learning companion rather than something to integrate into a pipeline.