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ABOUT THE COURSE:
This course introduces the fundamental principles of deep learning, from shallow models to advanced neural networks, with a strong focus on aerial perception. Students will learn core CNN architectures, training strategies, and methods for object detection, segmentation, and generative modeling. The course also covers online learning, localization, 3D reconstruction, and deep reinforcement learning. Emphasis is placed on practical skills, including data annotation and model deployment using MATLAB and PyTorch
INTENDED AUDIENCE: UG/ Working Professional
PREREQUISITES: Linear Algebra, Probability
This course introduces the fundamental principles of deep learning, from shallow models to advanced neural networks, with a strong focus on aerial perception. Students will learn core CNN architectures, training strategies, and methods for object detection, segmentation, and generative modeling. The course also covers online learning, localization, 3D reconstruction, and deep reinforcement learning. Emphasis is placed on practical skills, including data annotation and model deployment using MATLAB and PyTorch
INTENDED AUDIENCE: UG/ Working Professional
PREREQUISITES: Linear Algebra, Probability
