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    • Computer Vision

    필터링 기준

    "computer vision"에 대한 318개의 결과

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      Columbia University

      First Principles of Computer Vision

      획득할 기술: Computer Vision, Machine Learning, Computer Graphic Techniques, Computer Graphics, Mathematics, Algorithms, Theoretical Computer Science, Applied Machine Learning, Artificial Neural Networks, Deep Learning, Mathematical Theory & Analysis, Algebra

      4.7

      (93개의 검토)

      Beginner · Specialization · 3-6 Months

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      DeepLearning.AI

      Deep Learning

      획득할 기술: Deep Learning, Machine Learning, Artificial Neural Networks, Python Programming, Statistical Programming, Machine Learning Algorithms, Linear Algebra, Applied Machine Learning, Statistical Machine Learning, Dimensionality Reduction, Feature Engineering, Probability & Statistics, Business Psychology, Entrepreneurship, Machine Learning Software, Computer Vision, Marketing, General Statistics, Natural Language Processing, Computer Programming, Leadership and Management, Project Management, Regression, Sales, Strategy, Strategy and Operations, Tensorflow, Differential Equations, Mathematics, Applied Mathematics, Decision Making, Supply Chain Systems, Supply Chain and Logistics, Advertising, Communication, Estimation, Forecasting, Mathematical Theory & Analysis, Statistical Visualization, Algorithms, Theoretical Computer Science, Bayesian Statistics, Calculus, Probability Distribution, Statistical Tests, Big Data, Computer Architecture, Computer Networking, Data Management, Human Computer Interaction, Network Architecture, User Experience, Algebra, Computational Logic, Computer Graphic Techniques, Computer Graphics, Data Structures, Data Visualization, Hardware Design, Interactive Design, Markov Model, Network Model

      4.8

      (137.8k개의 검토)

      Intermediate · Specialization · 3-6 Months

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      IBM Skills Network

      Introduction to Computer Vision and Image Processing

      획득할 기술: Computer Vision, Machine Learning, Computer Graphics, Computer Graphic Techniques, Algorithms, Artificial Neural Networks, Deep Learning, Theoretical Computer Science, Applied Machine Learning, IBM Cloud, Machine Learning Software

      4.4

      (885개의 검토)

      Beginner · Course · 1-3 Months

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      DeepLearning.AI

      Advanced Computer Vision with TensorFlow

      획득할 기술: Computer Vision, Deep Learning, Machine Learning, Tensorflow, Applied Machine Learning, Computer Programming, Python Programming, Statistical Programming, Computer Graphic Techniques, Computer Graphics

      4.8

      (394개의 검토)

      Intermediate · Course · 1-4 Weeks

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      MathWorks

      Computer Vision for Engineering and Science

      획득할 기술: Computer Vision, Machine Learning, Data Analysis, Matlab

      5.0

      (6개의 검토)

      Intermediate · Specialization · 1-3 Months

    • 무료

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      Edge Impulse

      Computer Vision with Embedded Machine Learning

      획득할 기술: Computer Vision, Machine Learning, Artificial Neural Networks, Deep Learning, Estimation, General Statistics, Machine Learning Algorithms, Probability & Statistics, Computer Programming, Python Programming

      4.7

      (86개의 검토)

      Intermediate · Course · 1-4 Weeks

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      University of Toronto

      Self-Driving Cars

      획득할 기술: Computer Programming, Python Programming, Algorithms, Machine Learning, Theoretical Computer Science, Applied Machine Learning, Computer Science, Data Science, General Statistics, Probability & Statistics, Probability Distribution, Artificial Neural Networks, Linear Algebra, Mathematics, Application Development, Applied Mathematics, Calculus, Computational Logic, Differential Equations, Geometry, Machine Learning Algorithms, Software Engineering, Communication, Computer Graphic Techniques, Computer Graphics, Computer Networking, Computer Vision, Decision Making, Deep Learning, Entrepreneurship, Estimation, Feature Engineering, Graph Theory, Leadership and Management, Machine Learning Software, Mathematical Theory & Analysis, Network Model, Planning, Statistical Programming, Supply Chain and Logistics

      4.7

      (3.2k개의 검토)

      Advanced · Specialization · 3-6 Months

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      Coursera Project Network

      Deep Learning with PyTorch : Image Segmentation

      획득할 기술: Computer Programming, Computer Vision, Deep Learning, Machine Learning, Python Programming, Statistical Programming

      4.2

      (71개의 검토)

      Intermediate · Guided Project · Less Than 2 Hours

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      University at Buffalo

      Computer Vision Basics

      획득할 기술: Computer Vision, Machine Learning, Mathematics, Computer Graphics, Data Analysis, Matlab

      4.2

      (1.8k개의 검토)

      Intermediate · Course · 1-4 Weeks

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      DeepLearning.AI, Stanford University

      Machine Learning

      획득할 기술: Machine Learning, Probability & Statistics, Machine Learning Algorithms, General Statistics, Theoretical Computer Science, Algorithms, Applied Machine Learning, Artificial Neural Networks, Regression, Econometrics, Computer Programming, Deep Learning, Python Programming, Statistical Programming, Mathematics, Tensorflow, Data Management, Data Structures, Statistical Machine Learning, Reinforcement Learning, Probability Distribution, Mathematical Theory & Analysis, Data Analysis, Data Mining, Linear Algebra, Computer Vision, Calculus, Feature Engineering, Bayesian Statistics, Operations Research, Research and Design, Strategy and Operations, Computational Logic, Accounting, Communication

      4.9

      (8k개의 검토)

      Beginner · Specialization · 1-3 Months

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      Microsoft

      Microsoft Azure Data Scientist Associate (DP-100)

      획득할 기술: Machine Learning, Cloud Computing, Microsoft Azure, Machine Learning Algorithms, Probability & Statistics, Theoretical Computer Science, Algorithms, Apache, Big Data, Data Management, General Statistics, Computer Programming, Statistical Programming, Python Programming, Regression, Applied Machine Learning, Artificial Neural Networks, Computer Vision, Deep Learning, Bayesian Statistics, Business Analysis, Data Analysis, Exploratory Data Analysis, Extract, Transform, Load, Statistical Machine Learning, Strategy and Operations

      4.5

      (158개의 검토)

      Intermediate · Professional Certificate · 3-6 Months

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      Columbia University

      Camera and Imaging

      획득할 기술: Computer Vision, Computer Graphic Techniques, Computer Graphics, Machine Learning, Mathematics, Algorithms, Theoretical Computer Science, Mathematical Theory & Analysis

      4.6

      (72개의 검토)

      Beginner · Course · 1-3 Months

    computer vision과(와) 관련된 검색

    computer vision - image basics with opencv and python
    computer vision basics
    computer vision in microsoft azure
    computer vision with embedded machine learning
    computer vision for engineering and science
    computer vision fundamentals with google cloud
    advanced computer vision with tensorflow
    introduction to computer vision
    1234…27

    요약하자면, 여기에 가장 인기 있는 computer vision 강좌 10개가 있습니다.

    • First Principles of Computer Vision: Columbia University
    • Deep Learning: DeepLearning.AI
    • Introduction to Computer Vision and Image Processing: IBM Skills Network
    • Advanced Computer Vision with TensorFlow: DeepLearning.AI
    • Computer Vision for Engineering and Science: MathWorks
    • Computer Vision with Embedded Machine Learning: Edge Impulse
    • Self-Driving Cars: University of Toronto
    • Deep Learning with PyTorch : Image Segmentation: Coursera Project Network
    • Computer Vision Basics: University at Buffalo
    • Machine Learning: DeepLearning.AI

    Software Development에서 학습할 수 있는 스킬

    프로그래밍 언어 (34)
    Google (25)
    컴퓨터 프로그램 (21)
    소프트웨어 테스트 (21)
    웹 (19)
    Google Cloud Platform (18)
    애플리케이션 프로그래밍 인터페이스 (17)
    데이터 구조 (16)
    문제 해결 (14)
    객체 지향 프로그래밍 (13)
    Kubernetes (10)
    목록 및 레이블 (10)

    컴퓨터 비전에 대한 자주 묻는 질문

    • Computer Vision is the branch of Computer Science—particularly Machine Learning and AI—that has applications in many industries such as self-driving cars, robotics, augmented reality, face detection in law-enforcement agencies, and more. Essentially, it’s a robot analogue of human vision in which information about the environment is received by one or more video cameras and processed by a computer.

      Computer Vision solves a lot of problems, making it important to learn. Some of its uses include advances in health technologies. Computer Vision algorithms can help automate tasks such as detecting cancerous moles in skin images or finding symptoms in x-ray and MRI scans.‎

    • Thanks to a need for quality inspection in vision-guided robotic systems, the market for Computer Vision is anticipated to rise to $17.4 billion by 2024. To reap the benefits of this in-demand field, learners can enjoy opportunities as Computer Vision Engineers, Computer Vision Software Engineers, Applied Research Scientists, Computer Vision Testing Engineers, Deep Learning Engineer, Computer Vision Data Scientist, and more.‎

    • Computer Vision courses offered through Coursera equip learners with knowledge in how computers see and interpret the world as humans do; core concepts of Computer Vision and human vision capabilities; key application areas of Computer Vision and Digital Image Processing; Machine Learning and AI basics; and more.

      Lessons on Computer Vision are taught by Data Scientists, Software Engineers, and other specialists, and are administered via video lectures, readings, quizzes, hands-on projects, and more.‎

    • Before you begin studying computer vision, you’ll want to have a familiarity with mathematical analysis and linear algebra as well as a knowledge of Python syntax, the TensorFlow Deep Learning Framework, and OpenCV library. Any advanced experience in computer programming will benefit you as you learn computer vision, particularly if you understand application programming interfaces. You’ll also have an advantage in studying computer vision if you have knowledge of artificial intelligence and machine learning. Other mathematical skills that will help you include statistics and geometry. And a working knowledge of biological vision will help you have a better understanding of how computer vision works.‎

    • Computer vision is just a small slice of the quickly growing field of machine learning, and people with experience in artificial intelligence or machine learning, in general, are well suited for job opportunities in this discipline. Anyone with a background in machine learning can take their skills and specifically hone them for a computer vision career. Data scientists who have deep learning, data structure, and programming language knowledge have a foundation that will benefit them as they study computer vision. Any programmer or scientist who has experience with digital image processing computer programs and algorithms has grasped the basic fundamentals to embark on a career in computer vision.‎

    • Computer vision is an exciting field with possibilities in health care, app development, and data security, and if you’re interested in being on the leading edge of any of those industries, you may want to consider learning computer vision. Learning computer vision is right for anybody who wants to develop platforms and programming languages that allow computers to see the world the way humans do. You might find the idea of analyzing large numbers of images and building visual systems that read the data and context of those images for web-based apps and computer programs to recognize appeals to you, and if so, learning computer vision is likely right for you.‎

    이 FAQ 콘텐츠는 정보 전달 목적만으로 사용할 수 있습니다. 학습자는 과정 및 기타 학점 정보가 개인적, 직업적 및 재정적 목표에 부합하는지 확인하기 위해 추가 조사를 수행하는 것이 좋습니다.
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