| 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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