Computer Vision with OpenCV | Deep Learning CNN Projects




Computer Vision with OpenCV | Deep Learning CNN Projects

OpenCV-Python is an appropriate tool for fast prototyping of Computer Vision problems.

Hands-on Computer Vision with OpenCV from scratch to real-time project development.

Computer Vision is the hottest field in the era of Artificial Intelligence. It is making enormous advances in Self-driving cars, Robotics, Medical as well as in various image correction apps.

Computer Vision is showing us the future of technology and we can't even imagine what will be the end of its possibilities.

OpenCV library uses NumPy and all its array structures convert to and from NumPy arrays.

OpenCV is used to develop real-time computer vision applications. It is capable of processing images and videos to identify objects, faces, or even handwriting.

You will learn the topics:

  • The key concepts of computer Vision & OpenCV

  • Basic operations: Image read and display, Image Properties, Image resize and write, ROI and Color Mapping, Horizontal & Vertical flipping of images.

  • Drawing function in OpenCV

  • Working with Live Camera

  • Object detection: haarcascade face detection in images and Live camera

  • Convolutional Neural Network (CNN)

  • Deep Learning with Keras

  • Project: Handwritten Digit Classification using MNIST

  • Project: Fashion Classifier using FNIST

  • Project: Dogs vs Cats classifier

  • Project: Object Detection using YOLOv3

  • Project: Social Distancing Detector COVID-19

Feel free to message us on Udemy if you have any questions about the course!

So, are you ready to get started? Enroll now and start the process of becoming a Master in Computer Vision using Deep Learning today!

Learn Python OpenCV 4,Computer Vision and Deep Learning Projects from scratch to expert level| Deep Learning CNN Project

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What you will learn
  • Computer vision and Image Processing
  • Image Processing operations using Opencv
  • Face Detection and work with Live Camera

Rating: 4.2

Level: All Levels

Duration: 5 hours

Instructor: Goeduhub Technologies


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