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Computer Science
Artificial Intelligence
Deep Learning
Deep Learning for Computer Vision
1. Foundations of Computer Vision and Deep Learning
2. Convolutional Neural Networks
3. Training Deep Vision Models
4. Classical CNN Architectures
5. Modern CNN Architectures
6. Core Computer Vision Tasks
7. Advanced Topics and Applications
8. Practical Implementation and Deployment
Core Computer Vision Tasks
Image Classification
Problem Formulation
Single-label Classification
Multi-label Classification
Hierarchical Classification
Network Architecture Design
Feature Extraction Backbone
Classification Head
Output Layer Configuration
Training Strategies
Loss Function Selection
Optimization Techniques
Regularization Methods
Evaluation Metrics
Accuracy
Top-k Accuracy
Precision and Recall
F1 Score
Area Under Curve
Confusion Matrix Analysis
Class Imbalance Handling
Weighted Loss Functions
Sampling Strategies
Cost-sensitive Learning
Object Detection
Problem Definition
Localization and Classification
Multiple Object Handling
Bounding Box Representation
Coordinate Systems
Box Parameterization
Two-stage Detectors
R-CNN
Region Proposal
Feature Extraction
Classification and Regression
Fast R-CNN
ROI Pooling
Multi-task Loss
End-to-end Training
Faster R-CNN
Region Proposal Network
Anchor Generation
Non-maximum Suppression
Single-stage Detectors
YOLO Family
YOLOv1
Grid-based Prediction
Bounding Box Regression
YOLOv2
Anchor Boxes
Batch Normalization
YOLOv3
Multi-scale Prediction
Feature Pyramid
YOLOv4
Bag of Freebies
Bag of Specials
YOLOv5
Implementation Improvements
SSD
Multi-scale Feature Maps
Default Boxes
Hard Negative Mining
RetinaNet
Feature Pyramid Network
Focal Loss
Anchor-based vs Anchor-free
Anchor Design Challenges
CenterNet
FCOS
Evaluation Metrics
Intersection over Union
Average Precision
Mean Average Precision
COCO Evaluation Protocol
Semantic Segmentation
Problem Definition
Pixel-wise Classification
Dense Prediction
Fully Convolutional Networks
Removing Fully Connected Layers
Upsampling Strategies
Skip Connections
Encoder-Decoder Architectures
U-Net
Symmetric Architecture
Skip Connections
Medical Image Applications
SegNet
Pooling Indices
Memory Efficiency
DeepLab Family
Atrous Convolutions
Atrous Spatial Pyramid Pooling
CRF Post-processing
Multi-scale Processing
Pyramid Pooling Module
Feature Pyramid Networks
Evaluation Metrics
Pixel Accuracy
Mean Intersection over Union
Frequency Weighted IoU
Dice Coefficient
Instance Segmentation
Problem Definition
Object Detection and Segmentation
Instance Differentiation
Mask R-CNN
ROI Align
Mask Prediction Branch
Multi-task Loss
PANet
Path Aggregation Network
Adaptive Feature Pooling
YOLACT
Real-time Instance Segmentation
Prototype Generation
Evaluation Metrics
Mask Average Precision
Boundary F1 Score
Panoptic Segmentation
Problem Definition
Stuff and Things
Unified Segmentation
Panoptic FPN
Semantic and Instance Branches
UPSNet
Unified Architecture
Evaluation Metrics
Panoptic Quality
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7. Advanced Topics and Applications