Об этом курсе: 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.

Для кого этот курс: The course is designed for people (1) who already know the basics of machine learning and deep learning (2) who have never studied image processing and computer vision and want to fill that gap (3) who want to learn how to solve computer vision problems with deep learning.

Автор: National Research University Higher School of Economics

Преподаватели: Anton Konushin, Senior Lecturer
HSE Faculty of Computer Science
Преподаватели: Alexey Artemov, Senior Lecturer
HSE Faculty of Computer Science

Основные сведения	Курс 5 из 7 — Advanced Machine Learning Specialization
Уровень	Продвинутые функции
Выполнение	5 weeks of study
Язык	Английский
Как пройти курс	Чтобы пройти курс, выполните все оцениваемые задания.
Оценки пользователей	4.0 звезды Средняя оценка пользователей: 4.0Посмотрите, что пишут учащиеся

Программа курса

НЕДЕЛЯ 1

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!

8 видео

Видео: Digital images
Видео: Structure of human eye and vision
Видео: Color models
Видео: Image processing goals and tasks
Видео: Contrast and brightness correction
Видео: Image convolution
Видео: Edge detection

Оцениваемый: Basic image processing

Оцениваемый: Image Alignment Notebook

НЕДЕЛЯ 2

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.

12 видео

Видео: AlexNet, VGG and Inception architectures
Видео: ResNet and beyond
Видео: Fine-grained image recognition
Видео: Detection and classification of facial attributes
Видео: Content-based image retrieval
Видео: Computing semantic image embeddings using convolutional neural networks
Видео: Employing indexing structures for efficient retrieval of semantic neighbors
Видео: Face verification
Видео: The re-identification problem in computer vision
Видео: Facial keypoints regression
Видео: CNN for keypoints regression

Оцениваемый: Convolutional features for visual recognition

Оцениваемый: Facial keypoints notebook

НЕДЕЛЯ 3

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.

13 видео

Видео: Sliding windows
Видео: HOG-based detector
Видео: Detector training
Видео: Viola-Jones face detector
Видео: Attentional cascades and neural networks
Видео: Region-based convolutional neural network
Видео: From R-CNN to Fast R-CNN
Видео: Faster R-CNN
Видео: Region-based fully-convolutional network
Видео: Single shot detectors
Видео: Speed vs. accuracy tradeoff
Видео: Fun with pedestrian detectors

Оцениваемый: Object Detection

Оцениваемый: Face detection notebook

НЕДЕЛЯ 4

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.

11 видео

Видео: Optical flow
Видео: Deep learning in optical flow estimation
Видео: Visual object tracking
Видео: Examples of visual object tracking methods
Видео: Multiple object tracking
Видео: Examples of multiple object tracking methods
Видео: Introduction to action recognition
Видео: Action classification
Видео: Action classification with convolutional neural networks
Видео: Action localization

Оцениваемый: Video Analysis

Оцениваемый: Face recognition notebook

НЕДЕЛЯ 5

Image segmentation and synthesis

In the last module of this course, we shall consider problems where the goal is to predict entire image. These are semantic image segmentation and image synthesis problems. Modern CNNs tailored for segmentation employ multiple specialised layers to allow for efficient training and inference. Lastly, we will get to know Generative Adversarial Networks — a bright new idea in machine learning, allowing to generate arbitrary realistic images.

7 видео

Видео: Oversegmentation
Видео: Deep learning models for image segmentation
Видео: Human pose estimation as image segmentation
Видео: Style transfer
Видео: Generative adversarial networks
Видео: Image transformation with neural networks

Оцениваемый: Image segmentation and synthesis

Оцениваемый: GAN notebook

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Как это работает

Trabajo del curso

Cada curso es como un libro de texto interactivo, con videos pregrabados, cuestionarios y proyectos.

Ayuda de tus compañeros

Conéctate con miles de estudiantes y debate ideas y materiales del curso, y obtén ayuda para dominar los conceptos.

Certificados

Obtén reconocimiento oficial por tu trabajo y comparte tu éxito con amigos, compañeros y empleadores.

Авторы

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

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Рейтинги и отзывы

Оценка 4 из 5 по 68 отзывам

Nice introductory course. It include many background knowledge of computer vision before deeplearning and is important to know.

Great content! Great assignments ideas. Problems with graders. Quizes and especially assignments, are loosely related to the lecture materials. The ability of authors to explain things is pretty week (it is not even close to Andrew Ng skills). The efforts necessary to complete assignments are ridiculously underestimated. You will need 10-20 hours or even more for each assignment, instead of 2hr specified. Total experience is very contradictory. I chose between 3 and 4. 3 is too low mark for these unique content, and creative assignment ideas, and I hope the assignment format could be improved in the feature, with not so many efforts (some tips, and intermediate assertions could help a lot). As for now, I could not recommend this course for wide audience, only for the most "desperate" students.