Course Summary
This course is designed to provide learners with an introduction to deep learning and computer vision. It covers key concepts such as convolutional neural networks, object detection, and image segmentation. By the end of the course, learners will be able to develop their own computer vision models using deep learning techniques.Key Learning Points
- Learn how to apply deep learning techniques to computer vision problems
- Understand key concepts such as convolutional neural networks and image segmentation
- Develop your own computer vision models using popular deep learning frameworks
Job Positions & Salaries of people who have taken this course might have
- USA: $100,000 - $150,000
- India: ₹1,000,000 - ₹1,500,000
- Spain: €30,000 - €50,000
- USA: $100,000 - $150,000
- India: ₹1,000,000 - ₹1,500,000
- Spain: €30,000 - €50,000
- USA: $80,000 - $120,000
- India: ₹800,000 - ₹1,200,000
- Spain: €25,000 - €40,000
- USA: $100,000 - $150,000
- India: ₹1,000,000 - ₹1,500,000
- Spain: €30,000 - €50,000
- USA: $80,000 - $120,000
- India: ₹800,000 - ₹1,200,000
- Spain: €25,000 - €40,000
- USA: $90,000 - $140,000
- India: ₹900,000 - ₹1,400,000
- Spain: €28,000 - €45,000
Related Topics for further study
Learning Outcomes
- Understand key concepts and techniques in deep learning and computer vision
- Be able to apply these techniques to real-world problems
- Develop your own computer vision models using popular deep learning frameworks
Prerequisites or good to have knowledge before taking this course
- Basic knowledge of Python programming
- Familiarity with linear algebra and calculus
Course Difficulty Level
IntermediateCourse Format
- Self-paced
- Online
- Video lectures
Similar Courses
- Applied Data Science with Python
- Deep Learning Specialization
- Computer Vision Basics
Related Education Paths
Related Books
Description
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.
Outline
- Introduction to image processing and computer vision
- About the University
- Short introduction to computer vision
- Digital images
- Structure of human eye and vision
- Color models
- Image processing goals and tasks
- Contrast and brightness correction
- Image convolution
- Edge detection
- About University
- Rules on the academic integrity in the course
- Basic image processing
- Convolutional features for visual recognition
- Recap: Image classification
- AlexNet, VGG and Inception architectures
- ResNet and beyond
- Fine-grained image recognition
- Detection and classification of facial attributes
- Content-based image retrieval
- Computing semantic image embeddings using convolutional neural networks
- Employing indexing structures for efficient retrieval of semantic neighbors
- Face verification
- The re-identification problem in computer vision
- Facial keypoints regression
- CNN for keypoints regression
- Convolutional features for visual recognition
- Object detection
- Object detection problem
- Sliding windows
- HOG-based detector
- Detector training
- Viola-Jones face detector
- Attentional cascades and neural networks
- Region-based convolutional neural network
- From R-CNN to Fast R-CNN
- Faster R-CNN
- Region-based fully-convolutional network
- Single shot detectors
- Speed vs. accuracy tradeoff
- Fun with pedestrian detectors
- Object Detection
- Object tracking and action recognition
- Introduction to video analysis
- Optical flow
- Deep learning in optical flow estimation
- Visual object tracking
- Examples of visual object tracking methods
- Multiple object tracking
- Examples of multiple object tracking methods
- Introduction to action recognition
- Action classification
- Action classification with convolutional neural networks
- Action localization
- Video Analysis
- Image segmentation and synthesis
- Image segmentation
- Oversegmentation
- Deep learning models for image segmentation
- Human pose estimation as image segmentation
- Style transfer
- Generative adversarial networks
- Image transformation with neural networks
- Image segmentation and synthesis
Summary of User Reviews
Discover the power of deep learning in computer vision with Coursera's comprehensive course. Students praise this course for its excellent content and practical applications.Key Aspect Users Liked About This Course
Practical applicationsPros from User Reviews
- Great content
- Practical examples
- In-depth explanations
- Easy to follow
- Excellent instructor
Cons from User Reviews
- Some sections are too technical
- Requires prior knowledge of machine learning
- Not suitable for beginners
- Lacks hands-on exercises
- Some videos are too long
Anton Konushin
3.8 Raiting
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