Hands-On Computer Vision with PyTorch 1.x [Video]
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.
About the Video Course
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.
What You Will 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
Instructions and Navigation
Assumed Knowledge
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.
Technical Requirements
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
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