Provide computer vision and build systems that rival human sight. Designed for beginners to computer vision or PyTorch.
About This Video
- Guides you through building state-of-the-art models that are used and developed by industry leaders
- Provides hands-on experience with quizzes and solutions to give you a deeper understanding of complex vision concepts
- Use the latest version of PyTorch to develop vision models
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.
Please note that a understanding of calculus and linear algebra, along with some experience using Python, are assumed for taking this course.
Table of contents
- Chapter 1 : Getting Started with Computer Vision and PyTorch
- Chapter 2 : Dive into Deep Learning with PyTorch
Chapter 3 : Working with Cost Functions and Optimizers
- Getting to Grips with Cost Functions 00:04:58
- Using Modern Optimizers 00:03:28
- Getting Familiar with Sequential API 00:02:13
- Working with Functional API 00:02:15
- Add Adam Optimizer to Handwritten Digit Recognizer 00:01:57
- Chapter 4 : Getting Started with Convolutions
- Chapter 5 : Deep Learning Building Blocks
- Chapter 6 : Loading and Manipulating Data with PyTorch
- Chapter 7 : Neural Style Transfer
- Chapter 8 : Generative Adversarial Networks
- Title: Hands-On Computer Vision with PyTorch 1.x
- Release date: March 2020
- Publisher(s): Packt Publishing
- ISBN: 9781789614077
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