Deep Learning for Images with PyTorch

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.