About this Course
최근 조회 4,186

다음의 4/4개 강좌

100% 온라인

지금 바로 시작해 나만의 일정에 따라 학습을 진행하세요.

탄력적인 마감일

일정에 따라 마감일을 재설정합니다.

중급 단계

Basic programming skills & experience; familiarity with basic linear algebra, calculus & probability, and 3D co-ordinate systems & transformations

완료하는 데 약 12시간 필요

권장: 4 weeks of study, 4-5 hours per week...

영어

자막: 영어

배울 내용

  • Check

    Understand machine learning techniques used in computer vision

  • Check

    Classify letters, objects and scenes

  • Check

    Detect and recognize faces

  • Check

    Solve computer vision problems with deep learning

귀하가 습득할 기술

Deep LearningMatlabMachine LearningComputer ProgrammingComputer Vision

다음의 4/4개 강좌

100% 온라인

지금 바로 시작해 나만의 일정에 따라 학습을 진행하세요.

탄력적인 마감일

일정에 따라 마감일을 재설정합니다.

중급 단계

Basic programming skills & experience; familiarity with basic linear algebra, calculus & probability, and 3D co-ordinate systems & transformations

완료하는 데 약 12시간 필요

권장: 4 weeks of study, 4-5 hours per week...

영어

자막: 영어

강의 계획 - 이 강좌에서 배울 내용

1
완료하는 데 4시간 필요

Introduction to Visual Recognition & Understanding

This module provides an introduction to visual recognition and understanding in Computer Vision....
9 videos (Total 30 min), 2 readings, 2 quizzes
9개의 동영상
Health Care & Visual Perception2m
Detection, Localization & Classification6m
Recognition7m
Product Identification30
Recognition: Progress & Unsolved Problems3m
More Unsolved Problems & Gaps1m
Machine Learning in Computer Vision31
Machine Learning: Past & Present1m
2개의 읽기 자료
Resources (Optional): Introduction to Visual Recognition & Understanding30m
REQUIRED- MATLAB and Deep Learning Onramp
1개 연습문제
Machine Learning for Computer Vision30m
2
완료하는 데 1시간 필요

Early Techniques

This module discusses optical character recognition, face detection, face recognition, and other early techniques used for visual recognition....
5 videos (Total 8 min), 1 reading, 1 quiz
5개의 동영상
Techniques: Before Deep Learning47
Adaboost for Face Detection1m
Eigenfaces for Face Recognition2m
SVMs for Object Detection1m
1개의 읽기 자료
Resources (Optional): Early Techniques30m
1개 연습문제
Training Neural Network30m
3
완료하는 데 1시간 필요

Deep Learning Overview

In this module, we will discuss the history of Deep Learning, how it is used, and how it is revolutionizing the field of Computer Vision....
6 videos (Total 12 min), 1 reading
6개의 동영상
Introduction to Deep Learning3m
Insight on Deep Learning48
Convolutional Neural Networks2m
LSTM, RNN & ResNet1m
Generative Models2m
1개의 읽기 자료
Resources (Optional) Deep Learning Overview30m
4
완료하는 데 1시간 필요

Deep Learning in Computer Vision: Applications

This module provides information about the various applications of Deep Learning in Computer Vision....
9 videos (Total 17 min), 2 readings
9개의 동영상
Deep Learning: Key Applications2m
Face Detection & Recognition1m
Image Segmentation1m
Video Understanding1m
Future of Computer Vision1m
Human-Machine Interaction1m
Future Research Areas3m
Evolution of Computer Vision2m
2개의 읽기 자료
Resources (Optional): Deep Learning in Computer Vision: Applications30m
Visual Recognition & Understanding - Key Takeaways10m

강사

Avatar

Radhakrishna Dasari

Instructor
Department of Computer Science
Avatar

Junsong Yuan

Associate Professor and Director of Visual Computing Lab
Computer Science and Engineering

뉴욕주립대학교 버펄로 캠퍼스 정보

The University at Buffalo (UB) is a premier, research-intensive public university and the largest, most comprehensive institution of the State University of New York (SUNY) system. UB offers more than 100 undergraduate degrees and nearly 300 graduate and professional programs....

뉴욕주립대학교 정보

The State University of New York, with 64 unique institutions, is the largest comprehensive system of higher education in the United States. Educating nearly 468,000 students in more than 7,500 degree and certificate programs both on campus and online, SUNY has nearly 3 million alumni around the globe....

컴퓨터 비전 전문 분야 정보

This specialization provides a foundation in the rapidly expanding research field of computer vision, laying the groundwork necessary for designing sophisticated vision applications. Learners explore the integral elements that enable vision applications, ranging from editing images to reading traffic signs in self-driving cars to factory robots navigating around human co-workers. Content includes image processing and state-of-the-art vision techniques, augmented by insights from top leaders in the computer vision field. Learners gain hands-on experience writing computer vision programs through online labs using MATLAB and supporting toolboxes. The specialization is taught in MATLAB* using computer vision and supporting toolboxes. Learners should have basic programming skills and experience (understanding of for loops, if/else statements), specifically in MATLAB (Mathworks provides the basics here: https://www.mathworks.com/learn/tutorials/matlab-onramp.html). Learners should also be familiar with the following: basic linear algebra (matrix vector operations and notation), 3D co-ordinate systems and transformations, basic calculus (derivatives and integration) and basic probability (random variables). To learn more, check out a video overview at https://youtu.be/OfxVUSCPXd0. * A free license to install MATLAB for the duration of the course is available from MathWorks....
컴퓨터 비전

자주 묻는 질문

  • 강좌에 등록하면 바로 모든 비디오, 테스트 및 프로그래밍 과제(해당하는 경우)에 접근할 수 있습니다. 상호 첨삭 과제는 이 세션이 시작된 경우에만 제출하고 검토할 수 있습니다. 강좌를 구매하지 않고 살펴보기만 하면 특정 과제에 접근하지 못할 수 있습니다.

  • 강좌를 등록하면 전문 분야의 모든 강좌에 접근할 수 있고 강좌를 완료하면 수료증을 취득할 수 있습니다. 전자 수료증이 성취도 페이지에 추가되며 해당 페이지에서 수료증을 인쇄하거나 LinkedIn 프로필에 수료증을 추가할 수 있습니다. 강좌 내용만 읽고 살펴보려면 해당 강좌를 무료로 청강할 수 있습니다.

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