Hands-on Computer Vision with PyTorch 1.x: Implement state-of-the art CV models in modern Pytorch

PyTorch 功能强大且易于使用。本课程将帮助您利用PyTorch的功能执行图像处理。本课程从介绍图像处理开始，向您介绍基本的深度学习和优化概念。接下来，您将学习使用 PyTorch 的 API，例如可用于图像分类的动态图计算张量。本课程从基本的 2D 图像开始，逐渐带您识别更复杂的图像、颜色、形状等。

使用 Python API，您将继续对模型进行分类和训练，以识别更复杂的图像——例如，识别植物物种比人类更好。然后，您将利用 PyTorch 的深度神经网络深入研究 AlexNet、ResNet、VGG-net、生成对抗网络 (GAN)、神经风格迁移等等。

参加本课程是您使用 PyTorch 将计算机视觉应用于项目的一站式实践指南。您将创建和部署自己的模型，并获得处理实际项目所需的直觉。

请注意，参加本课程的前提是了解微积分和线性代数，以及使用 Python 的一些经验。

h264, yuv420p, 1920×1080 |ENGLISH, aac, 48000 Hz, 2 channels, s16 | 2h 57 mn | 1.59 GB
Instructor: Colibri Ltd

Provide computer vision and build systems that rival human sight. Designed for beginners to computer vision or PyTorch.

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.

Please note that a understanding of calculus and linear algebra, along with some experience using Python, are assumed for taking this course.

All the code and supporting files for this course are available at https://github.com/PacktPublishing/Hands-On-Computer-Vision-with-PyTorch-1.x

Features

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