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Home > Programs > Course Description > Deep Learning for Digital Image Analysis
::: Deep Learning for Digital Image Analysis 3 credit 3 hours
This course covers the basics of deep learning for digital image analysis, including theoretical concepts and practical applications. Students will learn about object detection, image classification, and segmentation using state-of-the-art deep learning models. The course will also address the challenges of using deep learning in digital image analysis and cover popular image datasets such as MNIST, CIFAR-10, and ImageNet. Additionally, students will gain hands-on experience in image data preparation and labeling, and learn about evaluation metrics for deep learning models. Programming examples will be provided in Python or Matlab. The course includes: ●A gentle introduction to digital image analysis and deep learning. ●Convolutional neural networks. ●Transfer learning. ●Image data labeling for deep learning. ●Evaluating deep learning models. ●The challenges of deep learning in the field of digital image analysis. ●Activation functions. ●Loss functions. ●Optimizers.
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