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Deep Learning for Images with PyTorch
4.8(750)12k studentsADVANCEDEnglish
Korshub's take
The Korshub Take
This advanced computer vision course delivers a hands-on exploration of PyTorch across all major vision tasks. From Convolutional Neural Networks (CNNs) for image classification to advanced segmentation masks and Generative Adversarial Networks (GANs), it prepares practitioners for complex computer vision engineering tasks.
Who It's For
Advanced PyTorch practitioners, computer vision engineers, and machine learning research engineers seeking deep technical expertise in image processing and synthesis.
Key Takeaways
- CNNs & Object Detection: Train CNNs for binary and multi-class classification, leverage pre-trained models, and evaluate object detection using bounding boxes.
- Image Segmentation: Apply semantic, instance, and panoptic segmentation techniques using specialized masking architectures.
- Generative Adversarial Networks: Construct and train Deep Convolutional GANs (DCGANs) to synthesize images and assess generative quality and diversity.
About this course
This course on deep learning for images using PyTorch will equip you with the practical skills and knowledge to excel in image classification, object detection, segmentation, and generation.
Classify images with convolutional neural networks (CNNs)
You'll apply CNNs for binary and multi-class image classification and understand how to leverage pre-trained models in PyTorch. With bounding boxes, you'll also be able to detect objects within an image and evaluate the performance of object recognition models.Segment images by applying masks
Explore image segmentation, including semantic, instance, and panoptic segmentation, by applying masks to images and learn about the different model architectures needed for each type of segmentation.Generate images with GANs
Finally, you'll learn how to generate your own images using Generative Adversarial Networks (GANs). You'll learn the skills to build and train Deep Convolutional GANs (DCGANs) and how to assess the quality and diversity of generated images. By the end of this course, you'll have gained the skills and experience to work with various image tasks using PyTorch models.Advertisement