Course
Deep Learning for Images with PyTorch
AdvancedSkill Level
Updated 06/2025PyTorchArtificial Intelligence4 hr16 videos58 Exercises4,700 XP11,938Statement of Accomplishment
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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.Prerequisites
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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FAQs
What prior deep learning experience do I need?
This is an advanced course. You should have completed both Introduction to Deep Learning with PyTorch and Intermediate Deep Learning with PyTorch before starting.
What image tasks does this course cover beyond classification?
You will learn object detection with bounding boxes, image segmentation (semantic, instance, and panoptic), and image generation using Generative Adversarial Networks.
Does this course cover transfer learning?
Yes. You will use pre-trained models for deep learning image tasks, learning how transfer learning speeds up training and improves performance.
What are GANs and how are they covered?
GANs are Generative Adversarial Networks that create new images. You will build GANs in PyTorch and learn to assess the quality and diversity of generated images.
How long does this course take?
The course has 4 chapters and 58 exercises. Most learners finish it in about 2.5 to 3 hours.
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