metacritical · February 8, 2025 17:22 · Feb 8, 2025
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+# Computer Vision and Deep Learning Topics by Difficulty Level
+
+## Easy
+- [ ] FCN (Fully Convolutional Networks)
+- [ ] UNet: Standard architecture for biomedical image segmentation
+- [ ] YOLO Series: Real-time object detection family
+- [ ] CAM (Class Activation Mapping)
+- [ ] VGGNet
+- [ ] SqueezeNet: Lightweight architecture
+- [ ] EfficientNet: Scaling networks efficiently
+- [ ] ResNet: Residual Networks fundamentals
+- [ ] SSD (Single Shot Detector)
+- [ ] Basic Attention Mechanisms
+- [ ] Group Normalization
+- [ ] Transfer Learning Basics
+
+## Medium
+- [ ] Vision Transformer (ViT)
+- [ ] DETR (Detection Transformer)
+- [ ] RetinaNet
+- [ ] Mask R-CNN
+- [ ] FPN (Feature Pyramid Networks)
+- [ ] Yolov5 and Advanced YOLO variants
+- [ ] DeiT (Data-efficient image Transformer)
+- [ ] Graph Convolutional Networks
+- [ ] CenterNet
+- [ ] RepVGG
+- [ ] EfficientDet
+- [ ] Focal Loss and Advanced Loss Functions
+- [ ] Grad-CAM
+- [ ] DeepLab Series
+- [ ] Attention Mechanisms in Vision
+
+## Intermediate
+- [ ] StyleGAN Series
+- [ ] Swin Transformer
+- [ ] CLIP (Contrastive Language-Image Pre-training)
+- [ ] NeRF (Neural Radiance Fields)
+- [ ] Advanced Transformer Architectures
+- [ ] Graph Attention Networks
+- [ ] Panoptic Segmentation
+- [ ] Self-Attention and Multi-Head Attention
+- [ ] Advanced Object Detection
+- [ ] Instance Segmentation
+- [ ] Few-Shot Learning
+- [ ] Self-Supervised Learning
+- [ ] Metric Learning
+
+## Hard
+- [ ] Neural Architecture Search
+- [ ] Advanced GAN Architectures
+- [ ] Multi-Modal Learning
+- [ ] 3D Vision Transformers
+- [ ] Advanced NeRF Variants
+- [ ] Graph Neural Networks Theory
+- [ ] Meta-Learning
+- [ ] Self-Supervised Representation Learning
+- [ ] Advanced Optimization Techniques
+- [ ] Vision-Language Models
+- [ ] Quantum Computer Vision
+- [ ] Neural ODEs
+- [ ] Advanced Generative Models
+- [ ] Theoretical Deep Learning
+
+Note: Topics are categorized based on prerequisite knowledge, mathematical complexity, 
+and implementation difficulty. Individual topics may span multiple difficulty levels 
+depending on depth of study. To mark a topic first foork this list and the mark them 
+as completed, replace "[ ]" with "[x]" in the markdown.