After doing these courses, I feel confident creating professional visualizations and dashboards
Shoebur RahmanProcurement Specialist, Giz Bangladesh
Course Description
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.Prerequisites
Curriculum
Course outline
1
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.
- Binary and multi-class image classification50 XP
- The number of classes50 XP
- Binary classification model100 XP
- Multi-class classification model100 XP
- Convolutional layers for images50 XP
- RGB, grayscale, or alpha?50 XP
- Adding a new convolutional layer100 XP
- Creating a sequential block100 XP
- Working with pre-trained models50 XP
- Save and load a model100 XP
- Loading a pre-trained model100 XP
- Image classification with ResNet100 XP
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
Course
Complete
