PyTorch深度学习计算机视觉训练营 2024视频教程 (英文)

PyTorch深度学习计算机视觉训练营 2024

你将学到：

• 掌握如何使用深度学习进行计算机视觉任务
• 学习使用PyTorch
• Torch库中的卷积神经网络
• 在图像上建立卷积操作的直觉
• 在CIFAR10数据集上实现LeNet架构，该数据集包含60000张图像

要求：

• 基本的Python编程语言机器学习知识

介绍：

深入学习PyTorch中的计算机视觉：掌握深度学习、卷积神经网络和GPU计算，应用于现实世界的应用 – 2024版 解锁深度学习在计算机视觉中的潜力，颠覆性的进步正在塑造技术的未来。探索从Facebook的图像标记和Google照片的人物识别到欺诈检测和面部识别等应用。深入了解深度学习计算机视觉的核心操作，包括图像上的卷积操作，掌握从数字图像中提取有价值信息的艺术。

在这门全面的课程中，我们专注于最广泛使用的深度学习框架之一 – PyTorch。PyTorch被认为是产品原型和学术界中深度学习的首选工具，其Pythonic的特性、易学性、更高的开发者生产力、通过AutoGrad进行图计算的动态方法以及GPU支持的高效计算等方面脱颖而出。

为什么选择PyTorch？

• Pythonic：PyTorch与Python编程语言完美结合，为学习者提供自然而直观的体验。
• 易学性：PyTorch的简单性使其适用于初学者，让学习曲线更加平滑。
• 更高的开发者生产力：PyTorch的设计优先考虑了开发者的生产力，在构建和试验模型方面效率高。
• 动态图计算方法 – AutoGrad：PyTorch通过AutoGrad的动态计算图使得模型的开发更加灵活和高效。
• GPU支持：PyTorch提供GPU支持，加速计算，提高处理大型数据集和复杂模型的性能。

课程亮点：

• 获得PyTorch的基础理解，这对深入学习深度学习世界至关重要。
• 学习GPU编程，探索如何访问免费GPU资源以进行高效学习。
• 掌握PyTorch的AutoGrad功能，这是动态图计算的关键。
• 使用PyTorch实现深度学习模型，从理论过渡到实际应用。
• 在PyTorch中探索卷积神经网络（CNNs）的基础知识，这是计算机视觉任务的基本架构。
• 将CNNs应用于真实数据集，获得实际应用的动手经验。

我们的方法：

我们认为真正的学习不仅仅是理论理解；它还涉及通过实践应用来建立信心。在整个课程中，我们在每个部分的末尾都设置了作业，让您能够衡量自己的进步并加强学习。我们希望赋予您在计算机视觉的动态领域中所需的技能和信心。

与Manifold AI Learning一起踏上这个旅程，创新与教育相遇。我们期待着欢迎您加入课程，并见证您的成功。祝您好运！

Manifold AI Learning

适合人群：

• 软件开发人员
• 机器学习从业者
• 数据科学家
• 任何对学习PyTorch感兴趣的人
• 任何对深度学习感兴趣的人

PyTorch for Deep Learning Computer Vision Bootcamp 2024

Last updated 3/2024
Duration: 11h 42m | Video: .MP4, 1920×1080 30 fps | Audio: AAC, 48 kHz, 2ch | Size: 6.79 GB
Genre: eLearning | Language: English

Master Computer Vision in PyTorch/Python: Beginner to Pro with Expert Tips on Convolutional Neural Networks (CNNs)

What you’ll learn
Master how to Perform Computer Vision Task with Deep Learning
Learn to Work with PyTorch
Convolutional Neural Networks with Torch Library
Build Intuition on Convolution Operation on Images
Learn to Implement LeNet Architecture on CIFAR10 dataset which has 60000 images

Requirements
Basic Machine learning with Python Programming Language

Description
Dive into Computer Vision with PyTorch: Master Deep Learning, CNNs, and GPU Computing for Real-World Applications – 2024 Edition”
Unlock the potential of Deep Learning in Computer Vision, where groundbreaking advancements shape the future of technology. Explore applications ranging from Facebook’s image tagging and Google Photo’s People Recognition to fraud detection and facial recognition. Delve into the core operations of Deep Learning Computer Vision, including convolution operations on images, as you master the art of extracting valuable information from digital images.
In this comprehensive course, we focus on one of the most widely used Deep Learning frameworks – PyTorch. Recognized as the go-to tool for Deep Learning in both product prototypes and academia, PyTorch stands out for its Pythonic nature, ease of learning, higher developer productivity, dynamic approach for graph computation through AutoGrad, and GPU support for efficient computation.
Why PyTorch?
Pythonic: PyTorch aligns seamlessly with the Python programming language, offering a natural and intuitive experience for learners.
Easy to Learn: The simplicity of PyTorch makes it accessible for beginners, allowing a smooth learning curve.
Higher Developer Productivity: PyTorch’s design prioritizes developer productivity, promoting efficiency in building and experimenting with models.
Dynamic Approach for Graph Computation – AutoGrad: PyTorch’s dynamic computational graph through AutoGrad enables flexible and efficient model development.
GPU Support: PyTorch provides GPU support for accelerated computation, enhancing performance in handling large datasets and complex models.
Course Highlights
Gain a foundational understanding of PyTorch, essential for delving into the world of Deep Learning.
Learn GPU programming and explore how to access free GPU resources for efficient learning.
Master the AutoGrad feature of PyTorch, a key aspect for dynamic graph computation.
Implement Deep Learning models using PyTorch, transitioning from theory to practical application.
Explore the basics of Convolutional Neural Networks (CNNs) in PyTorch, a fundamental architecture for computer vision tasks.
Apply CNNs to real-world datasets, developing hands-on experience with practical applications.
Our Approach
We believe that true learning extends beyond theoretical understanding; it involves building confidence through practical application. Throughout the course, we’ve incorporated assignments at the end of each section, enabling you to measure your progress and reinforce your learning. We aspire to empower you with the skills and confidence needed to navigate the dynamic field of Deep Learning in Computer Vision.
Embark on this journey with Manifold AI Learning, where innovation meets education. We look forward to welcoming you inside the course and witnessing your success. Best of luck!
Manifold AI Learning
Who this course is for
Software Developer
Machine Learning Practitioner
Data Scientist
Anyone interested to learn PyTorch
Anyone interested in Deep learning

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