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Quick Facts

Medium Of Instructions	Mode Of Learning	Mode Of Delivery
English	Self Study	Video and Text Based

Course Overview

The Deep Learning in Computer Vision online course by Coursera is focused on introducing learners to computer vision, right from the basics. Eventually, the certification course moves to deep learning models which are more modern. Image, as well as video recognition, is a primary part of the programme. The comprehensive curriculum will cover image annotation and classification, motion estimation, human action recognition, and more.

Moreover, Deep Learning in Computer Vision programme is the fifth course in the Advanced Machine Learning Specialization. The National Research University Higher School of Economics offers an online course in association with Coursera. The Deep Learning in Computer Vision certification course is an advanced-level course with a self-paced learning option. You will also work on a final project to implement their learnings.

In the course project for the Deep Learning in Computer Vision course, you will learn to build face recognition and manipulation systems. This project will help you get an idea about the internal mechanics of this technology. The online course also includes a shareable certificate which you will receive from Coursera upon successful completion.

The Highlights

Shareable certificate
Flexible deadlines
Self-paced training
100% online
Financial aid available
Advanced-level course
Peer feedback
Final project
Discussion board

Programme Offerings

Graded Assignments
Hands on project
Shareable Certificate
Flexible Deadlines
Course completion certificate
advanced level course
Discussion Board

Courses and Certificate Fees

Certificate Availability	Certificate Providing Authority
yes	Coursera

The fee structure for the course is provided in the table below:

Deep Learning in Computer Vision fee structure

Enrollment Option	Fee (monthly)
Advanced Machine Learning Specialization	Rs 3,686

Eligibility Criteria

To get a certificate, candidates have to complete all the modules and assignments. In addition, they also have to get the minimum passing marks. When students fulfil these requirements, they will get a course completion certificate from Coursera, which can be attached to the LinkedIn page or resume.

What you will learn

Knowledge of Artificial IntelligenceKnowledge of deep learning

Once you finish the Deep Learning in Computer Vision online course, you will become proficient in the following:

Image and video recognition
Image search and object recognition, video, and image recognition
Different types of human action techniques object tracking in video, motion estimation, human action recognition
Lastly, stylization of image, new image generation and editing

Who it is for

AI DeveloperData AnalystData ScientistMachine Learning EngineerIT EngineerData Engineer

Admission Details

You can join the Deep Learning in Computer Vision programme by following the steps mentioned below:

Step 1. Visit the Course page.

Step 2. Look for “Deep Learning in Computer Vision” in the catalogue of Coursera. Click on the information page of the course.

Step3. Choose the ‘Enrol for free’ option.

Step 4. Follow the instructions to enrol in the course.

Step 5. Make the fee payment if you are interested in subscribing to the specialisation course and get easy access to all course materials. After you finish the enrolment process, you can begin the online course.

Application Details

To join the Deep Learning in Computer Vision course, share your name and email. Enter a password to finish your login process. Start the course once the enrollment process is complete. You can sign in with your Coursera/Google/Facebook ID. first-time users also get a seven-day free trial.

The Syllabus

About University
Digital images
Image processing- goals and tasks
A short introduction to computer vision
Image convolution
Structure of the human eye and vision
Edge detection
Colour models
Brightness correction and contrast

Image Classification recap
Fine-grained image recognition
AlexNet, VGG, and Inception architectures
ResNet and beyond
Compute semantic image embeddings with convolutional neural networks
Face verification
Detection and classification of facial attributes
Re-identification problem in computer vision
Facial keypoints regression
Content-based image retrieval
Employ indexing structures for efficient retrieval of semantic neighbours
CNN for key points regression

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

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

Over-segmentation
Image segmentation
Deep learning models for image segmentation
Style transfer
Human pose estimation as image segmentation
Image transformation using neural networks
Generative adversarial network

HSE University Frequently Asked Questions (FAQ's)

1: What can I do in case of technical difficulties?

If you face technical problems, you can write your queries to coursera@hse.ru.

2: Can I preview the study material?

You can either opt for an audit, wherein you can view the syllabus and course materials for free.

3: What is the duration of the course?

The Deep Learning in Computer Vision course has modules spread out over five weeks. Candidates will need 13 hours to complete the course.

4: What is the difficulty level of the Deep Learning in Computer Vision course?

The Deep Learning in Computer Vision certification course is an advanced-level course.

5: What are the weekly deadlines for the modules?

The online course is flexible and completed according to the convenience of the students. The course also enables students to reset deadlines, complete assignments, and listen to lectures at your own pace.