This is the code repository for Hands-On Computer Vision with PyTorch 1.x. It contains all the supporting project files necessary to work through the video course from start to finish.
This course is your one-stop, hands-on guide to applying computer vision to your projects using PyTorch. You'll learn to create and deploy your own models and develop the intuition to work on real-world projects.
- 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
To fully benefit from the coverage included in this course, you will need:
A basic knowledge of machine learning will help you understand the necessary concepts but isn't mandatory. A basic understanding of calculus and linear algebra; some experience using Python.
This course has the following software requirements:
- Operating system: windows 10
- Browser: Chrome, Firefox or IE
- SSH VS Code IDE, Latest Version
- Python 3.7 installed