Deep Learning for Computer Vision
Completed by Felix Matusch
October 19, 2025
14 hours (approximately)
Felix Matusch's account is verified. Coursera certifies their successful completion of Deep Learning for Computer Vision
What you will learn
Improve model performance and training stability using multilayer perceptrons (MLPs) and applying normalization techniques.
Implement autoencoders for unsupervised feature learning and design Generative Adversarial Networks (GANs) to generate synthetic images.
Train convolutional neural networks (CNNs) for image classification tasks, understanding how layers extract spatial features from visual data.
Apply advanced architectures like ResNet for deep image recognition and U-Net for image segmentation.
Skills you will gain
- Category: Generative Model Architectures
- Category: Convolutional Neural Networks
- Category: Autoencoders
- Category: Classification Algorithms
- Category: Dimensionality Reduction
- Category: Model Training
- Category: Generative Adversarial Networks (GANs)
- Category: Model Deployment
- Category: Unsupervised Learning
