An Introduction to Object Detection and YOLO

Learning Objectives Learn what’s YOLO and how it’s different from other approaches to object detection L...

Stone River eLearning

Content Provider

7 mins

Duration

Online

Mode Of Delivery

12 months

Course Validity

Advanced

Level

Go1 - KRACKiN

Certification By

  • ( ₹9999 ₹2999)
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Description

Learning Objectives



Learn what’s YOLO and how it’s different from other approaches to object detection




  • Learn the basics of object detection

  • Discover other deep learning object detection methods

  • Learn about YOLO and its pre-trained models that you can use in your own project



This video forms part of the course Hands-On Deep Learning for Computer Vision



Course Overview



Machine Learning, and Deep learning techniques in particular, are changing the way computers see and interact with the World. From augmented and mixed-reality applications to just gathering data, these new techniques are revolutionizing a lot of industries This course is designed to give you a hands-on learning experience by going from the basic concepts to the most current in-depth Deep Learning methods for Computer Vision in use today.In this course, you will be introduced to the concept of deep learning and a variety of popular and effective techniques for image classification, detection, segmentation and generation. You will learn to build your own neural network and classify images accordingly. You will be taken through popular techniques such as Deep Dream (to generate psychedelic, surreal images), Style Transfer (to transfer styles between images), and Neural Doodle, to generate an image that matches a doodled sketch.By the end of this course, you will be able to use computer vision and deep learning to encode, classify, detect, and style images for the real world.



Target Audience



This course is for Python developers, with some OpenCV experience, who want to incorporate deep learning techniques into their computer vision work. Basic Python and Computer Vision knowledge is assumed


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