| Description
| 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.
|
|---|