Computer Vision Essentials

 4.47 (220 Ratings)

Skills covered

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

Course Syllabus

Computer Vision Essentials 

  • Working with Images_Introduction
  • 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)

Computer Vision Essentials

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