Prerequisites: Strong Python programming skills, solid understanding of machine learning fundamentals, experience with deep learning frameworks (TensorFlow or PyTorch), and familiarity with basic CNN concepts are required.

Level: Advanced

What You'll Learn

• 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.