Learn
Go from a beginner in the field of computer vision to an advanced practitioner with real-world insights
Take advantage of PyTorch's functionalities such as tensors, dynamic graphs, auto-differentiation, and more
Explore various computer-vision sub-topics, such as Conv nets, ResNets, Neural Style Transfer, data augmentation, and more
Build state-of-the-art, industrial image classification algorithms
Effortlessly split, augment, and draw conclusions from datasets
Extract information effortlessly from groundbreaking research papers
About
PyTorch is powerful and simple to use. This course will help you leverage the power of PyTorch to perform image processing. Beginning with an introduction to image processing, the course introduces you to basic deep-learning and optimization concepts. Next, you'll learn to use PyTorch's APIs such as the dynamic graph computation tensor, which can be used for image classification. Starting off with basic 2D images, the course gradually takes you through recognizing more complex images, color, shapes, and more.
Using the Python API, you'll move on to classifying and training your model to identify more complex images-for example, recognizing plant species better than humans. Then you'll delve into AlexNet, ResNet, VGG-net, Generative Adversarial Networks(GANs), neural style transfer, and more--all by taking advantage of PyTorch's Deep Neural Networks.
Taking this course is your one-stop, hands-on guide to applying computer vision to your projects using PyTorch. You'll create and deploy your own models, and gain the necessary intuition to work on real-world projects.