课程信息
4,759 次近期查看

第 4 门课程(共 4 门)

100% 在线

立即开始,按照自己的计划学习。

可灵活调整截止日期

根据您的日程表重置截止日期。

中级

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

完成时间大约为10 小时

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

英语(English)

字幕:英语(English)

您将学到的内容有

  • 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

您将获得的技能

Deep LearningMatlabMachine LearningComputer ProgrammingComputer Vision

第 4 门课程(共 4 门)

100% 在线

立即开始,按照自己的计划学习。

可灵活调整截止日期

根据您的日程表重置截止日期。

中级

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

完成时间大约为10 小时

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

英语(English)

字幕:英语(English)

教学大纲 - 您将从这门课程中学到什么

1
完成时间为 4 小时

Introduction to Visual Recognition & Understanding

9 个视频 (总计 30 分钟), 2 个阅读材料, 2 个测验
9 个视频
Recognition7分钟
Product Identification30
Recognition: Progress & Unsolved Problems3分钟
More Unsolved Problems & Gaps1分钟
Machine Learning in Computer Vision31
Machine Learning: Past & Present1分钟
2 个阅读材料
Resources (Optional): Introduction to Visual Recognition & Understanding30分钟
REQUIRED- MATLAB and Deep Learning Onramp2小时
1 个练习
Machine Learning for Computer Vision30分钟
2
完成时间为 1 小时

Early Techniques

5 个视频 (总计 8 分钟), 1 个阅读材料, 1 个测验
5 个视频
Eigenfaces for Face Recognition2分钟
SVMs for Object Detection1分钟
1 个阅读材料
Resources (Optional): Early Techniques30分钟
1 个练习
Training Neural Network30分钟
3
完成时间为 1 小时

Deep Learning Overview

6 个视频 (总计 12 分钟), 1 个阅读材料
6 个视频
Convolutional Neural Networks2分钟
LSTM, RNN & ResNet1分钟
Generative Models2分钟
1 个阅读材料
Resources (Optional) Deep Learning Overview30分钟
4
完成时间为 1 小时

Deep Learning in Computer Vision: Applications

9 个视频 (总计 17 分钟), 2 个阅读材料
9 个视频
Image Segmentation1分钟
Video Understanding1分钟
Future of Computer Vision1分钟
Human-Machine Interaction1分钟
Future Research Areas3分钟
Evolution of Computer Vision2分钟
2 个阅读材料
Resources (Optional): Deep Learning in Computer Vision: Applications30分钟
Visual Recognition & Understanding - Key Takeaways10分钟

讲师

Avatar

Radhakrishna Dasari

Instructor
Department of Computer Science
Avatar

Junsong Yuan

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

关于 纽约州立大学布法罗分校

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....

关于 纽约州立大学纽约州立大学

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....

关于 计算机视觉 专项课程

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....
计算机视觉

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