Whether you want to build systems that can classify images, detect and locate objects, segment visual scenes, or even generate entirely new images, this course gives you the practical skills to do it all. Starting with the foundations of convolutional neural networks, you'll progressively work through the core pillars of modern computer vision — image classification, object detection with architectures like YOLO and Faster R-CNN, and image segmentation with models like U-Net and Mask R-CNN — before diving into generative AI with Stable Diffusion, including techniques like inpainting, ControlNet, and LoRA adapters.
Every topic is taught hands-on using industry-standard tools like PyTorch, Torchvision, Albumentations, Ultralytics, and Hugging Face Diffusers, and the course wraps up with mini projects that challenge you to combine everything into complete, end-to-end solutions.
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