Computer Vision Essentials

Computer Vision takes a fascinating glimpse into the future of machine learning.It basically evolves a bunch of convolutional neural networks using a genetic algorithm to create an optimal network for image classification.

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About the course

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Computer vision has become prevalent in our society with its applications spanning across multiple domains like medicine, mapping, drones, and self-driving cars.

New developments in the neural network field have drastically improved the speed at which these images are processed and the performance of these state-of-the-art visual recognition systems. This course focuses on how to build a convolutional neural network to approach the problem of image recognition and fine-tune the network for optimized performance.

Skills you will gain

  • Working with Images
  • Convolution
  • Pooling
  • CNN
  • Convolutional Neural Networks
  • Transfer learning

Course Syllabus

Module 1

Computer Vision Essentials

5.5 Hrs

1 Quiz
  • Working with Images - Digitization, Sampling, and Quantization
  • Working with images - Filtering
  • Introduction to Convolutions
  • 2D convolutions for Images
  • Convolution - Forward
  • Convolution - Backward
  • Transposed Convolution and Fully Connected Layer as a Convolution
  • Pooling : Max Pooling and Other pooling options
  • CNN Architectures and LeNet Case Study
  • GPU vs CPU
  • Transfer Learning Principles and Practice
  • Visualization (run pacakge, occlusion experiment)
  • Show more


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Course certificate

Get Computer Vision Essentials course completion certificate from Great learning which you can share in the Certifications section of your LinkedIn profile, on printed resumes, CVs, or other documents.