Deep Learning for Computer Vision: CNNs and Advanced Tools
Master deep learning for computer vision. Learn CNN architectures, image classification, and object detection using TensorFlow and Python.
Master deep learning for computer vision. Learn CNN architectures, image classification, and object detection using TensorFlow and Python.
Dive into the world of Computer Vision and Deep Learning with this comprehensive course. You'll explore both classic and modern approaches to Computer Vision tasks, focusing on Deep Learning applications. Learn about Convolutional Neural Networks (CNNs), image classification, object detection, and advanced deep learning tools. Gain hands-on experience with TensorFlow and Python libraries, analyzing results and comparing traditional and deep learning methods. This course is ideal for those with a background in calculus and linear algebra, looking to apply deep learning to computer vision problems.
4.6
(64 ratings)
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What you'll learn
Explain Computer Vision concepts and provide examples of various Computer Vision tasks
Describe classic algorithmic solutions to Computer Vision problems and analyze their pros and cons
Use modern machine learning tools and Python libraries for Computer Vision applications
Understand the architecture and components of Convolutional Neural Networks (CNNs)
Build, train, and apply neural networks and CNNs for image classification using TensorFlow
Analyze and compare the performance of classic Computer Vision methods and Deep Learning approaches
Skills you'll gain
This course includes:
7.48 Hours PreRecorded video
4 quizzes
Access on Mobile, Tablet, Desktop
FullTime access
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There are 5 modules in this course
This course provides a comprehensive introduction to Deep Learning applications in Computer Vision. Students will explore both classic Computer Vision techniques and modern Deep Learning approaches. The curriculum covers fundamental concepts of Computer Vision tasks, neural networks, and Convolutional Neural Networks (CNNs). Participants will gain hands-on experience using TensorFlow and Python libraries to build, train, and apply deep neural networks for image classification and other Computer Vision tasks. The course emphasizes practical skills and analysis of results, comparing traditional methods with deep learning techniques.
Introduction and Background
Module 1 · 4 Hours to complete
Classic Computer Vision Tools
Module 2 · 4 Hours to complete
Image Classification in Computer Vision
Module 3 · 2 Hours to complete
Neural Networks and Deep Learning
Module 4 · 4 Hours to complete
Convolutional Neural Networks and Deep Learning Advanced Tools
Module 5 · 6 Hours to complete
Fee Structure
Payment options
Financial Aid
Instructor
Associate Teaching Professor and Chair of Undergraduate Education
Dr. Ioana Fleming is an Associate Teaching Professor and the Chair of Undergraduate Education in the Computer Science Department at the University of Colorado Boulder. Originally from Romania, she earned her MD before pursuing advanced degrees, including an MS and Ph.D. from Johns Hopkins University. Dr. Fleming's research focuses on the intersection of computer vision and medical imaging, with specific applications in ultrasound elastography, thermal imaging, and image-guided surgery. Her work aims to enhance medical technologies through innovative imaging techniques, contributing significantly to both academia and the field of healthcare technology. As a dedicated educator, she plays a crucial role in shaping the undergraduate curriculum and fostering a supportive learning environment for students in computer science.
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4.6 course rating
64 ratings
Frequently asked questions
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