Deep Learning for Images with PyTorch

Apply PyTorch to images and use deep learning models for object detection with bounding boxes and image segmentation generation.

In this course, you'll use PyTorch to discover image classification, object recognition, segmentation, and image generation. You'll work with both binary and multi-class image classification models, utilize pre-trained models for deep learning tasks, and master object detection with bounding boxes. Additionally, you'll delve into image segmentation, including semantic, instance, and panoptic types, with real-world applications. Finally, you'll explore Generative Adversarial Networks (GANs) and learn to assess the quality and diversity of the generated images.

• 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.
• Object recognition
• Detect objects in images by predicting bounding boxes around them and evaluate the performance of object recognition models.
• 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.
• 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.