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Deep Learning in Computer Vision

Anton Konushin and Alexey Artemov
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...
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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 online 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 the 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 often demonstrated in movies and TV-shows example of computer vision and AI. Do you have technical problems? Write to us: [email protected]
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Good to know

Know what's good
, what to watch for
, and possible dealbreakers
Suitable for learners with aspiring computer scientists and AI experts
Provides a good balance of theoretical understanding and hands-on experience
Taught by reputable instructors with extensive experience in the field
covers a wide range of topics, from basic computer vision concepts to advanced deep learning models
Offers a hands-on project to build a face recognition system, providing practical experience

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Reviews summary

Computer vision concepts

A graduate-level course in computer vision. Coursework follows technical developments and includes image recognition, 3D reconstruction, and more. Assignments include implementing object detectors and face recognition. There is a mixture of positive and negative feedback. Be aware of issues with peer grading and assignments written in Python 2.
Builds Practical Skills
"You'll implement basic edge detectors from scratch, perform image correction, keypoints regression, face detection and recognition and you'll also implement image generation!"
"Pros : 1. Fantastic real-life assignments, which require knowledge and coding ability to build and fine tune realistic deep learning models."
Lack of Instructor Support
"no support present. "
"Teachers are absent from the forums, assignments contain broken links to the datasets, and the video content is read mechanically by the teachers."
Unclear Expectations
"This course is a non-starter. no clear explant=ations and even in my first week I have already spent more time trying to understand what is being asked of me rather than learning."
"this course is really hard to follow, the lectrurer basically reads from the slides instead of explaining, plus it is really hard to follow him with his accent."
Peer Graded Assignments
"assignments are not uniform. it did not give a uniform environments."
"There is absolutely some valuable content in here, but it's overwhelmed by the course's problems."
"I'm going to have to abandon this entire sequence because of this course."
"assignments are very badly structured."
Unclear Presentations
"I found it useful as a very general overview. I could then go to other sources and the specific technical and research literature and go from there."
"Also the dojo are rater 1h or 2h or so, they are in general really tricky and require a lot of experimenting, and often many hours of training. Witch does not match the anounced time requirement."
"Lectures are a cute introduction but not a profundization."
Outdated Materials
"The coding examples generally don't work. Out of date."
"Three of the first four programming assignments are in python2 for no reason, with no reason to think they'll be updated as the packages get more and more out of date and the hoops you have to jump through get more and more obscure."
"Sometimes the length of the videos are long and concepts presented are so unclear that, searching and learning by myself would take way longer than estimated time of 1 week."
Challenging Assignments
"By far, this course has the most challenging assignments in the specialization with an inherent relationship among several weeks project. Took hours and hours to beat the passing score."
"4, But when you finished the course, you will learn a lot things."

Career center

Learners who complete Deep Learning in Computer Vision will develop knowledge and skills that may be useful to these careers:
Computer Vision Engineer
A Computer Vision Engineer designs, develops, and maintains computer vision systems and applications. Increasingly, this role focuses on deep learning models. This course helps establish a strong foundation for this work by covering image and video recognition, image classification, and annotation. You will also get hands-on experience with object recognition, image search, human action recognition, and image stylization.
Data Scientist
Data Scientists apply advanced analytical and machine learning techniques to solve complex problems. In many organizations, this role uses computer vision as part of the solution to these problems. This course provides you foundational knowledge of computer vision, including deep learning techniques. You will learn how to use these techniques to build models for a variety of real-world problems.
Software Engineer
Software Engineers design, develop, and maintain software systems. As more organizations use computer vision, Software Engineers who specialize in this area are in high demand. This course helps build a foundation for this specialization by teaching the fundamentals of computer vision and deep learning-based models.
Machine Learning Engineer
Machine Learning Engineers design, develop, and maintain machine learning models. These models are often used for computer vision applications. This course provides you with the foundational knowledge you need to be successful in this role. You will learn about deep learning, image and video recognition, object recognition, and image search.
Deep Learning Engineer
Deep Learning Engineers design, develop, and maintain deep learning models. These models are used in various applications, including computer vision. This course provides you with the foundation you need to be successful in this role. You will learn about the fundamentals of deep learning, image and video recognition, and object detection.
Artificial Intelligence Engineer
Artificial Intelligence Engineers design, develop, and maintain artificial intelligence systems. These systems often use computer vision as a component. This course provides you with the foundational knowledge you need to be successful in this role. You will learn about deep learning, image and video recognition, object recognition, and image search.
Computer Scientist
Computer Scientists conduct research and development in the field of computer science. This includes work in computer vision. This course provides you with the foundational knowledge you need to be successful in this role. You will learn about deep learning, image and video recognition, and object detection.
Image Processing Engineer
Image Processing Engineers design, develop, and maintain image processing systems. This includes work in computer vision. This course provides you with the foundational knowledge you need to be successful in this role. You will learn about deep learning, image and video recognition, and object detection.
Robotics Engineer
Robotics Engineers design, develop, and maintain robots. Many of these robots use computer vision as a core component. This course provides you with the foundational knowledge you need to be successful in this role. You will learn about deep learning, image and video recognition, and object detection.
Computer Vision Consultant
Computer Vision Consultants provide advice and guidance to organizations on how to use computer vision technology. This course provides you with the foundational knowledge you need to be successful in this role. You will learn about deep learning, image and video recognition, and object detection.
Computer Vision Researcher
Computer Vision Researchers conduct research in the field of computer vision. This course provides you with the foundational knowledge you need to be successful in this role. You will learn about deep learning, image and video recognition, and object detection.
Visual Effects Artist
Visual Effects Artists use computer graphics to create realistic images for film, television, and other media. This often involves using computer vision techniques. This course provides you with the foundational knowledge you need to be successful in this role. You will learn about deep learning, image and video recognition, and object detection.
Product Manager
Product Managers develop and manage products. This often involves working with computer vision technology. This course may be useful for those who want to work with computer vision in a product management role. You will learn about the fundamentals of computer vision, image recognition, and object detection.
Business Analyst
Business Analysts help organizations improve their business processes. This often involves using data analysis and visualization techniques. Those interested in working in business analysis may find this course useful. You will learn how to use computer vision to analyze data and visualize results.
Technical Writer
Technical Writers create documentation for software and other technical products. This often involves explaining complex technical concepts. Those interested in writing about computer vision may find this course useful. You will learn the fundamentals of computer vision, including deep learning, image recognition, and object detection.

Reading list

We've selected 0 books that we think will supplement your learning. Use these to develop background knowledge, enrich your coursework, and gain a deeper understanding of the topics covered in Deep Learning in Computer Vision.

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