Build sophisticated computer vision systems using advanced Convolutional Neural Network techniques. This course covers complex architectures, optimization strategies, and specialized applications like object detection, segmentation, and generation. Focuses on practical implementation and performance tuning.
Prerequisites Python, ML/DL, CNN basics.
Level:
Advanced Architectures
Implement and customize state-of-the-art CNN architectures like ResNet, DenseNet, and EfficientNet.
Training Optimization
Apply advanced training and regularization techniques, including modern optimizers and learning rate schedules.
Object Detection
Develop models for object detection using techniques like Faster R-CNN, YOLO, and SSD.
Image Segmentation
Implement models for semantic and instance segmentation using FCNs, U-Net, and Mask R-CNN.
Attention and Transformers
Understand and implement attention mechanisms and Vision Transformers (ViT) in vision models.
Transfer Learning
Utilize advanced transfer learning and domain adaptation strategies effectively for specialized domains.
Generative Models
Explore generative models like GANs (DCGAN, StyleGAN) for image synthesis tasks.
Model Efficiency
Apply model optimization and compression techniques such as pruning, quantization, and knowledge distillation.