关于此课程： 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.
此课程适用人群： 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.
教学方： Anton Konushin, Senior Lecturer
HSE Faculty of Computer Science教学方： Alexey Artemov, Senior Lecturer
HSE Faculty of Computer Science
| 基本信息 | 课程 5（共 7 门，Advanced Machine Learning Specialization ） |
| 级别 | 高级设置 |
| 承诺学习时间 | 5 weeks of study |
| 语言 | English |
| 如何通过 | 通过所有计分作业以完成课程。 |
| 用户评分 |
- 视频: Digital images
- 视频: Structure of human eye and vision
- 视频: Color models
- 视频: Image processing goals and tasks
- 视频: Contrast and brightness correction
- 视频: Image convolution
- 视频: Edge detection
- 视频: AlexNet, VGG and Inception architectures
- 视频: ResNet and beyond
- 视频: Fine-grained image recognition
- 视频: Detection and classification of facial attributes
- 视频: Content-based image retrieval
- 视频: Computing semantic image embeddings using convolutional neural networks
- 视频: Employing indexing structures for efficient retrieval of semantic neighbors
- 视频: Face verification
- 视频: The re-identification problem in computer vision
- 视频: Facial keypoints regression
- 视频: CNN for keypoints regression
- 视频: Sliding windows
- 视频: HOG-based detector
- 视频: Detector training
- 视频: Viola-Jones face detector
- 视频: Attentional cascades and neural networks
- 视频: Region-based convolutional neural network
- 视频: From R-CNN to Fast R-CNN
- 视频: Faster R-CNN
- 视频: Region-based fully-convolutional network
- 视频: Single shot detectors
- 视频: Speed vs. accuracy tradeoff
- 视频: Fun with pedestrian detectors
- 视频: Optical flow
- 视频: Deep learning in optical flow estimation
- 视频: Visual object tracking
- 视频: Examples of visual object tracking methods
- 视频: Multiple object tracking
- 视频: Examples of multiple object tracking methods
- 视频: Introduction to action recognition
- 视频: Action classification
- 视频: Action classification with convolutional neural networks
- 视频: Action localization
- 视频: Oversegmentation
- 视频: Deep learning models for image segmentation
- 视频: Human pose estimation as image segmentation
- 视频: Style transfer
- 视频: Generative adversarial networks
- 视频: Image transformation with neural networks
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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.
Excellent course! Quiz questions are conceptual and challenging and assignments are pretty rigorous and 100% practical application oriented.
WX
Nice introductory course. It include many background knowledge of computer vision before deeplearning and is important to know.