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所在平台: Udemy |
课程主页: https://www.udemy.com/course/computervision-deeplearning-with-python/
课程评论：没有评论
课程名称：现代计算机视觉与深度学习（Python & PyTorch） 课程概述： 欢迎参加“现代计算机视觉与深度学习（Python & PyTorch）”课程！本课程旨在教会您如何让计算机像人类一样“看”和理解视觉世界。计算机视觉是人工智能（AI）的一个领域，使计算机能够观察和解释环境。课程将为您提供在主要计算机视觉问题中应用深度学习技术的全面实践经验，包括图像分类、语义分割、实例分割和对象检测。 在课程中，您将从计算机视觉和深度学习的基础开始，学习如何使用Python和PyTorch实现、训练、测试、评估和部署自己的模型。计算机视觉在自动驾驶车辆的发展中扮演着关键角色，能够帮助车辆感知和理解其周围环境，安全有效地导航。 课程将深入探讨计算机视觉在日常生活中的应用，包括人脸检测、指纹识别、互动游戏、医学影像等。您将使用Python和PyTorch建立、训练和测试主要计算机视觉问题的模型，获得大量实践经验。 学习内容： - 计算机视觉和深度学习基本概念，及其在现实中的应用 - 深度卷积神经网络（CNN）在计算机视觉中的应用 - 图像分类：单标签和多标签分类 - 迁移学习技术的应用 - 数据增强技术 - 语义分割深度学习架构及其在自动驾驶汽车中的应用 - 对象检测及使用Deep Learning架构（如RCNN系列）进行检测 - 使用Detectron2进行自定义对象检测 适合对象： 本课程适合计算机视觉工程师、人工智能爱好者、机器学习和深度学习工程师、数据科学家、以及对计算机视觉和深度学习感兴趣的研究人员和学生。无论您是行业专业人士还是初学者，本课程将为您提供所需的技能和知识，提升您在这一领域的竞争力。 结语： 加入现代计算机视觉和深度学习的革命，开拓您的技能，推动创新，开创无限可能的征程。完成本课程后，您将具备在自己的工作或研究中应用深度学习于计算机视觉问题的能力，让我们一起开始这段令人兴奋的旅程吧！
Welcome to the course "Modern Computer Vision & Deep Learning with Python & PyTorch"! Imagine being able to teach computers to see just like humans. Computer Vision is a type of artificial intelligence (AI) that enables computers and machines to see the visual world, just like the way humans see and understand their environment. Artificial intelligence (AI) enables computers to think, where Computer Vision enables computers to see, observe and interpret. This course is particularly designed to provide a comprehensive, hands-on experience in applying Deep Learning techniques to major Computer Vision problems including Image Classification, Semantic Segmentation, Instance Segmentation, and Object Detection. In this course, you'll start with an introduction to the basics of Computer Vision and Deep Learning, and learn how to implement, train, test, evaluate and deploy your own models using Python and PyTorch for Image Classification, Image Segmentation, and Object Detection. Computer Vision plays a vital role in the development of autonomous vehicles. It enables the vehicle to perceive and understand its surroundings to detect and classify various objects in the environment, such as pedestrians, vehicles, traffic signs, and obstacles. This helps to make informed decisions for safe and efficient vehicle navigation. Computer Vision is used for Surveillance and Security using drones to track suspicious activities, intruders, and objects of interest. It enables real-time monitoring and threat detection in public spaces, airports, banks, and other security-sensitive areas. Today Computer Vision applications in our daily life are very common including Face Detection in cameras and cell phones, logging in to devices with fingerprints and face recognition, interactive games, MRI, CT scans, image guided surgery and much more. This comprehensive course is especially designed to give you hands-on experience using Python and Pytorch coding to build, train, test and deploy your own models for major Computer Vision problems including Image Classification, Image Segmentation (Semantic Segmentation and Instance Segmentation), and Object Detection. So, are you ready to unleash the power of Computer Vision and Deep Learning with Python and PyTorch:Master the cutting-edge techniques and algorithms driving the field of Computer Vision.Dive deep into the world of Deep Learning and gain hands-on experience with Python and PyTorch, the industry-leading framework.Discover the secrets behind building intelligent systems that can understand, interpret, and make decisions from visual data.Unlock the power to revolutionize industries such as healthcare, autonomous systems, robotics, and more.Gain practical skills through immersive projects, real-world applications, and hands-on coding exercises.Gain insights into best practices, industry trends, and future directions in computer vision and deep learning.What You'll Learn:This course covers the complete pipeline with hands-on experience of Computer Vision tasks using Deep Learning with Python and PyTorch as follows:Introduction to Computer Vision and Deep Learning with real-world applicationsLearn Deep Convolutional Neural Networks (CNN) for Computer VisionYou will use Google Colab Notebooks for writing the python and Pytorch code.Perform two types of Image Classification using Deep Learning models with Python. Single-label Classification.Multi-label Classification.You will be able to learn Transfer Learning techniques:Transfer Learning by FineTuning the Model.Transfer Learning by using the Model as Fixed Feature Extractor.You will learn how to perform Data Augmentation.You will Learn to FineTune the Deep Resnet Model.You will learn how to use the Deep Resnet Model as Fixed Feature Extractor. You will Learn HyperParameters Optimization and results visualization.Semantic Image Segmentation and its Real-World Applications in Self Driving Cars or Autonomous Vehicles etc.Deep Learning Architectures for Semantic Segmentation including: UNet, and UNet++Pyramid Scene Parsing Network (PSPNet), Pyramid Attention Network (PAN), Multi-Task Contextual Network (MTCNet), DeepLabV3, etc.Datasets and Data annotations Tool for Semantic SegmentationData Augmentation and Data Loading in PyTorch for Semantic SegmentationPerformance Metrics (IOU) for Segmentation Models EvaluationSegmentation Models Implementation in PyTorch using different Encoder and Decoder ArchitecturesHyperparameters Optimization and Training of Segmentation ModelsTest Segmentation Model and Calculate IOU, Class-wise IOU, Pixel Accuracy, Precision, Recall and F-scoreVisualize Segmentation Results and Generate RGB Predicted Segmentation MapLearn Object Detection using Deep Learning Models with PytorchLearn Object Detection Deep Learning Architecture:RCNN, Fast RCNN, Faster RCNNMask RCNN Perform Object Detection with Fast RCNN and Faster RCNNIntroduction to Detectron2 by Facebook AI Research (FAIR)Preform Object Detection with Detectron2 ModelsExplore Custom Object Detection Dataset with AnnotationsPerform Object Detection on Custom Dataset using Deep LearningTrain, Test, Evaluate Your Own Object Detection Models and Visualize ResultsPerform Instance Segmentation using Mask RCNN on Custom Dataset with Pytorch and PythonWho Should Attend: This course is designed for a wide range of students and professionals, including but not limited to:Computer Vision Engineers, Artificial Intelligence AI enthusiasts and Researchers who want to learn how to use Python adn PyTorch to build, train and deploy Deep Learning models for Computer Vision problemsMachine Learning Engineers, Deep Learning Engineers, and Data Scientists who want to apply Deep Learning to Computer Vision tasks Developers who want to incorporate Computer Vision and Deep Learning capabilities into their projectsGraduates and Researchers in Computer Science, Electrical Engineering, and other related fields who want to learn about the latest advances in Deep Learning for Computer VisionIn general, the course is for Anyone who wants to learn how to use Deep Learning to extract meaning from visual data and gain a deeper understanding of the theory and practical applications of Computer Vision using Python and PyTorchThis course is designed for AI enthusiasts, data scientists, software engineers, researchers, and anyone passionate about unlocking the potential of computer vision and deep learning. Whether you're a seasoned professional or just starting your journey, this course will equip you with the skills and knowledge needed to excel in this rapidly evolving field.Join the Visionary Revolution: Don't miss out on this incredible opportunity to join the visionary revolution in modern Computer Vision & Deep Learning. Expand your skill set, push the boundaries of innovation, and embark on a transformative journey that will open doors to limitless possibilities. By the end of this course, you'll have the knowledge and skills you need to start applying Deep Learning to Computer Vision problems including Image Classification, Image Segmentation, and Object Detection in your own work or research. Whether you're a Computer Vision Engineer, or Developer, this course is the perfect way to take your understanding of Deep Learning to the next level. Let's get started on this exciting journey of Deep Learning for Computer Vision with Python and PyTorch.See you inside the Class!!
