course
Deep Learning for Images with PyTorch
AvanceradFärdighetsnivå
Uppdaterad 2025-06PyTorchArtificial Intelligence4 timmar16 videos58 exercises4,700 XP11,752Uttalande om prestation
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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.Förkunskapskrav
Intermediate Deep Learning with PyTorch1
Image Classification with CNNs
Learn about image classification with CNNs, the difference between the binary and multi-class image classification models, and how to use transfer learning for image classification in PyTorch.
2
Object Recognition
Detect objects in images by predicting bounding boxes around them and evaluate the performance of object recognition models.
3
Image Segmentation
Learn about the three types of image segmentation (semantic, instance, and panoptic), their applications, and the appropriate machine learning model architectures to perform each of them.
4
Image Generation with GANs
Generate completely new images with Generative Adversarial Networks (GANs). Learn to build and train a Deep Convolutional GAN, and how to evaluate the quality and variety of its outputs.
Deep Learning for Images with PyTorch
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