Informações sobre o curso
4.1
59 classificações
10 avaliações
Deep learning added a huge boost to the already rapidly developing field of computer vision. With deep learning, a lot of new applications of computer vision techniques have been introduced and are now becoming parts of our everyday lives. These include face recognition and indexing, photo stylization or machine vision in self-driving cars. The goal of this course is to introduce students to computer vision, starting from basics and then turning to more modern deep learning models. We will cover both image and video recognition, including image classification and annotation, object recognition and image search, various object detection techniques, motion estimation, object tracking in video, human action recognition, and finally image stylization, editing and new image generation. In course project, students will learn how to build face recognition and manipulation system to understand the internal mechanics of this technology, probably the most renown and oftenly demonstrated in movies and TV-shows example of computer vision and AI....
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cursos 100% online

Comece imediatamente e aprenda em seu próprio cronograma.
Calendar

Prazos flexíveis

Redefinir os prazos de acordo com sua programação.
Advanced Level

Nível avançado

Clock

Approx. 24 hours to complete

Sugerido: 5 weeks of study...
Comment Dots

English

Legendas: English...
Globe

cursos 100% online

Comece imediatamente e aprenda em seu próprio cronograma.
Calendar

Prazos flexíveis

Redefinir os prazos de acordo com sua programação.
Advanced Level

Nível avançado

Clock

Approx. 24 hours to complete

Sugerido: 5 weeks of study...
Comment Dots

English

Legendas: English...

Programa - O que você aprenderá com este curso

Week
1
Clock
3 horas para concluir

Introduction to image processing and computer vision

Welcome to the "Deep Learning for Computer Vision“ course! In the first introductory week, you'll learn about the purpose of computer vision, digital images, and operations that can be applied to them, like brightness and contrast correction, convolution and linear filtering. These simple image processing methods solve as building blocks for all the deep learning employed in the field of computer vision. Let’s get started!...
Reading
8 vídeos (Total de 54 min), 2 testes
Video8 videos
Digital images3min
Structure of human eye and vision6min
Color models15min
Image processing goals and tasks2min
Contrast and brightness correction5min
Image convolution7min
Edge detection8min
Quiz1 exercícios práticos
Basic image processing10min
Week
2
Clock
4 horas para concluir

Convolutional features for visual recognition

Module two revolves around general principles underlying modern computer vision architectures based on deep convolutional neural networks. We’ll build and analyse convolutional architectures tailored for a number of conventional problems in vision: image categorisation, fine-grained recognition, content-based retrieval, and various aspect of face recognition. On the practical side, you’ll learn how to build your own key-points detector using a deep regression CNN. ...
Reading
12 vídeos (Total de 91 min), 2 testes
Video12 videos
AlexNet, VGG and Inception architectures11min
ResNet and beyond10min
Fine-grained image recognition5min
Detection and classification of facial attributes6min
Content-based image retrieval7min
Computing semantic image embeddings using convolutional neural networks8min
Employing indexing structures for efficient retrieval of semantic neighbors9min
Face verification6min
The re-identification problem in computer vision5min
Facial keypoints regression6min
CNN for keypoints regression5min
Quiz1 exercícios práticos
Convolutional features for visual recognition24min
Week
3
Clock
3 horas para concluir

Object detection

In this week, we focus on the object detection task — one of the central problems in vision. We start with recalling the conventional sliding window + classifier approach culminating in Viola-Jones detector. Tracing the development of deep convolutional detectors up until recent days, we consider R-CNN and single shot detector models. Practice includes training a face detection model using a deep convolutional neural network....
Reading
13 vídeos (Total de 46 min), 2 testes
Video13 videos
Sliding windows3min
HOG-based detector2min
Detector training3min
Viola-Jones face detector5min
Attentional cascades and neural networks3min
Region-based convolutional neural network3min
From R-CNN to Fast R-CNN5min
Faster R-CNN4min
Region-based fully-convolutional network2min
Single shot detectors3min
Speed vs. accuracy tradeoff1min
Fun with pedestrian detectors1min
Quiz1 exercícios práticos
Object Detection16min
Week
4
Clock
4 horas para concluir

Object tracking and action recognition

The fourth module of our course focuses on video analysis and includes material on optical flow estimation, visual object tracking, and action recognition. Motion is a central topic in video analysis, opening many possibilities for end-to-end learning of action patterns and object signatures. You will learn to design computer vision architectures for video analysis including visual trackers and action recognition models....
Reading
11 vídeos (Total de 74 min), 2 testes
Video11 videos
Optical flow5min
Deep learning in optical flow estimation5min
Visual object tracking5min
Examples of visual object tracking methods13min
Multiple object tracking5min
Examples of multiple object tracking methods8min
Introduction to action recognition6min
Action classification7min
Action classification with convolutional neural networks5min
Action localization6min
Quiz1 exercícios práticos
Video Analysis16min
4.1

Melhores avaliações

por SJJun 12th 2018

Excellent course! Quiz questions are conceptual and challenging and assignments are pretty rigorous and 100% practical application oriented.

Instrutores

Anton Konushin

Senior Lecturer
HSE Faculty of Computer Science

Alexey Artemov

Senior Lecturer
HSE Faculty of Computer Science

Sobre National Research University Higher School of Economics

National Research University - Higher School of Economics (HSE) is one of the top research universities in Russia. Established in 1992 to promote new research and teaching in economics and related disciplines, it now offers programs at all levels of university education across an extraordinary range of fields of study including business, sociology, cultural studies, philosophy, political science, international relations, law, Asian studies, media and communications, IT, mathematics, engineering, and more. Learn more on www.hse.ru...

Sobre o Programa de cursos integrados Advanced Machine Learning

This specialization gives an introduction to deep learning, reinforcement learning, natural language understanding, computer vision and Bayesian methods. Top Kaggle machine learning practitioners and CERN scientists will share their experience of solving real-world problems and help you to fill the gaps between theory and practice. Upon completion of 7 courses you will be able to apply modern machine learning methods in enterprise and understand the caveats of real-world data and settings....
Advanced Machine Learning

Perguntas Frequentes – FAQ

  • Once you enroll for a Certificate, you’ll have access to all videos, quizzes, and programming assignments (if applicable). Peer review assignments can only be submitted and reviewed once your session has begun. If you choose to explore the course without purchasing, you may not be able to access certain assignments.

  • When you enroll in the course, you get access to all of the courses in the Specialization, and you earn a certificate when you complete the work. Your electronic Certificate will be added to your Accomplishments page - from there, you can print your Certificate or add it to your LinkedIn profile. If you only want to read and view the course content, you can audit the course for free.

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