Informações sobre o curso
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Comece imediatamente e aprenda em seu próprio cronograma.

Prazos flexíveis

Redefinir os prazos de acordo com sua programação.

Nível intermediário

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

Aprox. 12 horas para completar

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

Inglês

Legendas: Inglês

O que você vai aprender

  • 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

Habilidades que você terá

Deep LearningMatlabMachine LearningComputer ProgrammingComputer Vision

100% online

Comece imediatamente e aprenda em seu próprio cronograma.

Prazos flexíveis

Redefinir os prazos de acordo com sua programação.

Nível intermediário

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

Aprox. 12 horas para completar

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

Inglês

Legendas: Inglês

Programa - O que você aprenderá com este curso

Semana
1
4 horas para concluir

Introduction to Visual Recognition & Understanding

This module provides an introduction to visual recognition and understanding in Computer Vision.

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9 vídeos ((Total 30 mín.)), 2 leituras, 2 testes
9 videos
Health Care & Visual Perception2min
Detection, Localization & Classification6min
Recognition7min
Product Identification30s
Recognition: Progress & Unsolved Problems3min
More Unsolved Problems & Gaps1min
Machine Learning in Computer Vision31s
Machine Learning: Past & Present1min
2 leituras
Resources (Optional): Introduction to Visual Recognition & Understanding30min
REQUIRED- MATLAB and Deep Learning Onramp2h
1 exercício prático
Machine Learning for Computer Vision30min
Semana
2
1 hora para concluir

Early Techniques

This module discusses optical character recognition, face detection, face recognition, and other early techniques used for visual recognition.

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5 vídeos ((Total 8 mín.)), 1 leitura, 1 teste
5 videos
Techniques: Before Deep Learning47s
Adaboost for Face Detection1min
Eigenfaces for Face Recognition2min
SVMs for Object Detection1min
1 leituras
Resources (Optional): Early Techniques30min
1 exercício prático
Training Neural Network30min
Semana
3
1 hora para concluir

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.

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6 vídeos ((Total 12 mín.)), 1 leitura
6 videos
Introduction to Deep Learning3min
Insight on Deep Learning48s
Convolutional Neural Networks2min
LSTM, RNN & ResNet1min
Generative Models2min
1 leituras
Resources (Optional) Deep Learning Overview30min
Semana
4
1 hora para concluir

Deep Learning in Computer Vision: Applications

This module provides information about the various applications of Deep Learning in Computer Vision.

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9 vídeos ((Total 17 mín.)), 2 leituras
9 videos
Deep Learning: Key Applications2min
Face Detection & Recognition1min
Image Segmentation1min
Video Understanding1min
Future of Computer Vision1min
Human-Machine Interaction1min
Future Research Areas3min
Evolution of Computer Vision2min
2 leituras
Resources (Optional): Deep Learning in Computer Vision: Applications30min
Visual Recognition & Understanding - Key Takeaways10min

Instrutores

Avatar

Radhakrishna Dasari

Instructor
Department of Computer Science
Avatar

Junsong Yuan

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

Sobre Universidade de Buffalo

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....

Sobre Universidade Estadual de Nova York

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....

Sobre o Programa de cursos integrados Visão computacional

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....
Visão computacional

Perguntas Frequentes – FAQ

  • Ao se inscrever para um Certificado, você terá acesso a todos os vídeos, testes e tarefas de programação (se aplicável). Tarefas avaliadas pelos colegas apenas podem ser enviadas e avaliadas após o início da sessão. Caso escolha explorar o curso sem adquiri-lo, talvez você não consiga acessar certas tarefas.

  • Quando você se inscreve no curso, tem acesso a todos os cursos na Especialização e pode obter um certificado quando concluir o trabalho. Seu Certificado eletrônico será adicionado à sua página de Participações e você poderá imprimi-lo ou adicioná-lo ao seu perfil no LinkedIn. Se quiser apenas ler e assistir o conteúdo do curso, você poderá frequentá-lo como ouvinte sem custo.

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