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CNN Fundamentals: Convolution, Pooling, and Architecture Design
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ResNet, Skip Connections, and Deep Network Design
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Transfer Learning for Computer Vision
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Object Detection: R-CNN, YOLO, and SSD
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Semantic and Instance Segmentation: FCN, U-Net, Mask R-CNN
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GANs for Computer Vision: Generator, Discriminator, DCGAN, CGAN, WGAN, CycleGAN
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Data Augmentation: Transformations, MixUp, CutMix, RandAugment, AutoAugment
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Vision Transformers (ViT) and Attention for Images
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Image Processing: Gradients, Filtering, Edge Detection, Frequency Domain, Histograms
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Diffusion Models for CV: DDPM, Noise Scheduling, U-Net for Denoising, Sampling
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BSDA5006 — Deep Learning for Computer Vision (DL-CV)
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