컴퓨터 비전 분야에서의 딥 러닝 응용 사례 from Coursera

이 강의는 CU 볼더 대학교의 데이터 과학 석사(MS-DS) 학위 과정의 일부로써 학점 인정이 가능하며 Coursera 플랫폼을 통해 제공됩니다. MS-DS는 CU 볼더 대학교의 응용 수학, 컴퓨터 과학, 정보 과학 및 기타 여러 학과 교수진이 모여 만든 학제간 학위 과정입니다. MS-DS는 능력에 따라 입학이 허가되고 지원 절차가 없기 때문에 컴퓨터 과학, 정보 과학, 수학 및 통계학 분야에 대해 광범위한 학부 과정을 이수하고 전문 경험이 풍부한 사람에게 이상적인 과정입니다. MS-DS 과정에 대한 정보는 링크(https://www.coursera.org/degrees/master-of-science-data-science-boulder)를 통해 확인하실 수 있습니다.

본 강의에서 여러분은 학습 및 연구 분야인 컴퓨터 비전에 대해 배웁니다. 먼저 고전적인 컴퓨터 비전 관점에서 몇 가지 컴퓨터 비전 과제와 추천 접근법을 살펴봅니다. 다음으로는 딥 러닝 기법을 소개하고 이를 동일한 문제에 적용해 보겠습니다. 그 후 결과를 분석하고 두 기법의 장단점을 알아보겠습니다. 또한 튜토리얼을 통해 최신 머신 러닝 툴 및 소프트웨어 라이브러리를 실습해봅니다. 딥 러닝을 활용할 수 있는 컴퓨터 비전 과제에는 이미지 분류, 국소화를 통한 이미지 분류, 객체 감지, 객체 분할, 얼굴 인식, 움직임 또는 자세 추정 등이 있습니다.

What's inside

Syllabus

강의 개요 및 배경

이번 단원에서는 컴퓨터 비전 분야를 알아봅니다. 컴퓨터 비전은 이미지에서 정보를 추출하는 것을 목표로 합니다. 컴퓨터 비전 과제의 주요 범주를 살펴본 뒤, 각 범주에 해당하는 응용 사례를 살펴보겠습니다. 머신 러닝 및 딥 러닝 기법의 도입이 컴퓨터 비전 분야에 어떤 영향을 주었는지 알아보겠습니다.

고전 컴퓨터 비전 툴

이번 단원에서는 고전적 컴퓨터 비전 툴 및 기법에 대해 알아봅니다. 합성곱 연산, 선형 필터, 그리고 이미지 특징을 감지하는 알고리즘을 알아봅니다.

컴퓨터 비전에서의 이미지 분류

이번 단원에서는 첫 번째로 고전 컴퓨터 비전에서의 객체 인식의 문제점을 복습합니다. 다음으로 고전 컴퓨터 비전 파이프라인을 통해 객체 인식 및 이미지 분류를 수행하는 단계를 살펴봅니다.

신경망 및 딥 러닝

이번 단원에서는 신경망을 사용한 이미지 분류 파이프라인이 고전 컴퓨터 비전 툴과 어떻게 다른지 비교해 보겠습니다. 그 후 신경망의 기본적인 요소에 대해 복습하겠습니다. 텐서 플로우 튜토리얼을 통해 이미지 분류 예측을 위해 신경망을 구축, 훈련 및 사용하는 방법을 실습하고 마무리 하겠습니다.

합성곱 신경망 및 딥 러닝 고급 툴

이번 단원에서는 합성곱 신경망의 구성 요소를 알아보겠습니다. 심층 신경망을 설명하는 매개변수와 초매개변수에 대해 배우고 이들이 어떻게 딥 러닝 모델의 정확도를 개선해주는지 알아보겠습니다. 텐서 플로우 튜토리얼을 통해 이미지를 분류하는 딥 신경망 구축, 훈련 및 사용을 실습하고 마무리 하겠습니다.

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Useful for backgrounds in statistics, mathematics, or computer science

Geared towards professionals with industry experience

Well-suited for beginners seeking a practical understanding of computer vision

Introduces real-world applications of deep learning in computer vision

Hands-on tutorials provide practical experience with computer vision tools

May require proficiency in TensorFlow for optimal learning experience

Activities

Be better prepared before your course. Deepen your understanding during and after it. Supplement your coursework and achieve mastery of the topics covered in 컴퓨터 비전 분야에서의 딥 러닝 응용 사례 with these activities:

Peer Tutoring: Computer Vision Concepts

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Offer peer tutoring sessions to support fellow students in understanding computer vision concepts, solidifying your own knowledge and fostering collaboration.

Browse courses on Computer Vision

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Identify areas where you have a strong understanding of computer vision concepts.
Prepare materials and resources for tutoring sessions.
Conduct peer tutoring sessions, explaining concepts and answering questions.
Receive feedback from students and adjust tutoring approach accordingly.

이미지 분류에 대한 소개

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기본적인 컴퓨터 비전 개념과 이미지 분류 기술을 간략히 검토합니다.

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컴퓨터 비전의 기본 개념에 대해 읽어보세요.
이미지 분류에 사용되는 일반적인 기술에 대해 알아보세요.

Image Segmentation and Object Detection

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Engage in practice drills to strengthen your skills in image segmentation and object detection, essential techniques in computer vision.

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Complete practice exercises in image segmentation.
Implement algorithms for object detection.
Analyze and interpret results of image segmentation and object detection.

Seven other activities

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이미지 분류 연습문제 풀기

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다양한 파라미터를 조절하여 이미지 분류 알고리즘의 성능을 개선합니다.

Browse courses on Image Classification

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다양한 이미지 분류 알고리즘을 사용하여 데이터셋에서 이미지 분류 수행
알고리즘의 입력 파라미터를 다양하게 수정하여 결과 성능 관찰

TensorFlow를 사용한 딥 러닝 이미지 분류 안내서

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이 안내서는 이미지 분류에 딥 러닝을 적용하는 데 필요한 기술을 개발하는 데 도움이 됩니다.

Browse courses on TensorFlow

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안내서 전체 읽기
코드 예제 따라하기
연습 문제 풀기

컴퓨터 비전 응용 사례 토론 및 공유

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실제 세계에서의 컴퓨터 비전의 다양한 응용 분야를 탐구하여 학습 범위를 확장하고 이해를 넓힙니다.

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컴퓨터 비전 응용 사례 조사
토론 그룹 또는 포럼을 통해 응용 사례 발표 및 토론

Deep Learning for Image Analysis

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Explore guided tutorials to gain hands-on experience in applying deep learning techniques to image analysis, a crucial aspect of computer vision.

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Follow guided tutorials on deep learning for image analysis.
Build and train deep learning models for image classification.
Evaluate the performance of deep learning models on image analysis tasks.

Computer Vision Project: Object Recognition

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Undertake a project to develop a computer vision system for object recognition, showcasing your understanding and practical application of computer vision concepts.

Browse courses on Object Recognition

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Define the scope and objectives of the object recognition project.
Gather and prepare a dataset for object recognition.
Design and implement a computer vision system for object recognition.
Evaluate the performance of the object recognition system.
Present and document the findings of the object recognition project.

Kaggle Competition: Image Classification

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Participate in a Kaggle competition focused on image classification to test your skills, learn from others, and contribute to the field of computer vision.

Browse courses on Kaggle Competition

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Join the Kaggle competition for image classification.
Explore the competition data and understand the task.
Develop and train machine learning models for image classification.
Submit your models to the competition and track your progress.
Analyze the results and learn from the competition experience.

이미지 분류 프로젝트 수행

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실제 데이터셋에 딥 러닝 기술을 적용하여 이미지 분류 모델을 구축하고 평가합니다.

Browse courses on Image Classification

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이미지 분류 프로젝트를 위한 데이터셋 수집 및 전처리
컨볼루션 신경망을 사용하여 이미지 분류 모델 구축
모델 훈련 및 평가

Career center

Learners who complete 컴퓨터 비전 분야에서의 딥 러닝 응용 사례 will develop knowledge and skills that may be useful to these careers:

Computer Vision Engineer

Computer Vision Engineers are in high demand due to the increasing use of AI in various industries. This course provides a strong foundation in computer vision techniques, including both classical and deep learning approaches. You will learn about image classification, object detection, and facial recognition, which are essential skills for Computer Vision Engineers. By taking this course, you will gain the knowledge and skills needed to succeed in this rapidly growing field.

See salaries and explore the career path for Computer Vision Engineer

Machine Learning Engineer

Machine Learning Engineers are responsible for designing, developing, and deploying machine learning models. This course provides a solid foundation in machine learning concepts, including deep learning. You will learn about supervised and unsupervised learning, feature engineering, and model evaluation. These skills are essential for Machine Learning Engineers who want to build and deploy successful machine learning models. This course can help you develop the skills and knowledge needed to succeed in this in-demand field.

See salaries and explore the career path for Machine Learning Engineer

Research Scientist

Computer Vision Researchers are responsible for developing new computer vision algorithms and techniques. This course provides a strong foundation in computer vision concepts and techniques. You will learn about image processing, feature extraction, and object recognition. These skills are essential for Computer Vision Researchers who want to develop new and innovative computer vision solutions. By taking this course, you will gain the knowledge and skills needed to succeed in this exciting field.

See salaries and explore the career path for Research Scientist

Deep Learning Engineer

Deep Learning Engineers are responsible for developing and deploying deep learning models. This course provides a solid foundation in deep learning concepts and techniques. You will learn about convolutional neural networks, recurrent neural networks, and generative adversarial networks. These skills are essential for Deep Learning Engineers who want to build and deploy successful deep learning models. By taking this course, you will gain the knowledge and skills needed to succeed in this rapidly growing field.

See salaries and explore the career path for Deep Learning Engineer

Software Engineer

Deep Learning Engineers are responsible for developing and deploying deep learning models to support AI projects. While skills in traditional software development are necessary, deep learning engineers must also have a strong understanding of deep learning theory. This course provides an immersive introduction to deep learning for software engineers who want to transition into a career developing deep learning solutions. Through this course, you will learn the fundamental concepts of deep learning and popular deep learning techniques. Upon completion of this course, you will be better prepared to join an AI team as a deep learning engineer.

See salaries and explore the career path for Software Engineer

Quality Assurance Analyst

Quality Assurance Analysts use computer vision techniques to test and verify the quality of products and services. This course provides a solid foundation in computer vision concepts and techniques. You will learn about image processing, feature extraction, and object recognition. These skills are essential for Quality Assurance Analysts who want to build and deploy quality assurance systems that can ensure the quality of products and services.

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Artificial Intelligence Engineer

Artificial Intelligence Engineers design, develop, and deploy AI systems. This course provides a solid foundation in AI concepts, including machine learning and deep learning. You will learn about natural language processing, computer vision, and speech recognition. These skills are essential for AI Engineers who want to build and deploy successful AI systems. By taking this course, you will gain the knowledge and skills needed to succeed in this rapidly growing field.

See salaries and explore the career path for Artificial Intelligence Engineer

Medical Imaging Analyst

Medical Imaging Analysts use computer vision techniques to analyze medical images. This course provides a solid foundation in computer vision concepts and techniques. You will learn about image processing, feature extraction, and object recognition. These skills are essential for Medical Imaging Analysts who want to develop new and innovative medical imaging solutions. By taking this course, you will gain the knowledge and skills needed to succeed in this exciting field.

See salaries and explore the career path for Medical Imaging Analyst

Autonomous Vehicle Engineer

Autonomous Vehicle Engineers design, develop, and deploy autonomous vehicles. This course provides a solid foundation in computer vision concepts and techniques. You will learn about image processing, feature extraction, and object recognition. These skills are essential for Autonomous Vehicle Engineers who want to build and deploy autonomous vehicles that can safely navigate the roads.

See salaries and explore the career path for Autonomous Vehicle Engineer

Marketing Manager

Marketing Managers are responsible for developing and executing marketing campaigns. This course provides a solid foundation in computer vision concepts and techniques. You will learn about image processing, feature extraction, and object recognition. These skills are essential for Marketing Managers who want to develop and execute marketing campaigns that use computer vision technology.

See salaries and explore the career path for Marketing Manager

Computer Vision Consultant

Computer Vision Consultants provide expert advice to businesses on how to use computer vision technology to solve business problems. This course provides a strong foundation in computer vision concepts and techniques. You will learn about image processing, feature extraction, and object recognition. These skills are essential for Computer Vision Consultants who want to help businesses use computer vision technology to improve their operations.

See salaries and explore the career path for Computer Vision Consultant

Data Scientist

Data Scientists use data to solve business problems. This course provides a strong foundation in data science techniques, including machine learning and deep learning. You will learn about data collection, data preprocessing, and data analysis. These skills are essential for Data Scientists who want to extract insights from data and solve real-world problems. By taking this course, you will gain the knowledge and skills needed to succeed in this exciting field.

See salaries and explore the career path for Data Scientist

Product Manager

Product Managers are responsible for developing and launching new products. This course provides a solid foundation in computer vision concepts and techniques. You will learn about image processing, feature extraction, and object recognition. These skills are essential for Product Managers who want to develop and launch new products that use computer vision technology.

See salaries and explore the career path for Product Manager

Robotics Engineer

Robotics Engineers design, develop, and deploy robots. This course provides a solid foundation in computer vision concepts and techniques. You will learn about image processing, feature extraction, and object recognition. These skills are essential for Robotics Engineers who want to build and deploy robots that can see and interact with the world around them. By taking this course, you will gain the knowledge and skills needed to succeed in this exciting field.

See salaries and explore the career path for Robotics Engineer

Security Analyst

Security Analysts use computer vision techniques to detect and prevent security breaches. This course provides a solid foundation in computer vision concepts and techniques. You will learn about image processing, feature extraction, and object recognition. These skills are essential for Security Analysts who want to build and deploy security systems that can protect businesses from cyber threats. By taking this course, you will gain the knowledge and skills needed to succeed in this rapidly growing field.

See salaries and explore the career path for Security Analyst

컴퓨터 비전 분야에서의 딥 러닝 응용 사례

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