Convolutional Neural Networks and Image Processing

Convolutional Neural Networks and Image Processing


Adam Kolář


  • basic knowledge of programing in Python
  • high school level of mathematics
  • Basics of machine learning on the level of our course Introduction to  machine Learning


Our workshop is for people who are looking for hands on experience with deep neural networks for image processing, but they didn’t have any real opportunity to do so yet. Through experiments, we will explore how and why such models work, what are the intuitions behind its’ functionality, and gradually, through simple examples, we’ll come to the models that are commonly used in industry. We will focus on possible use cases for neural net’s internal semantic image representation and how to visualize neural net behavior in the most effective way.


  • Back to the history
  • What the convolution is and why it works
  • TensorFlow (designing a simple convolutional neural network)
  • Practical classification task with the Fashion MNIST data set.
  • Experiments with the MSCOCO and ResNet data sets
  • Visualisations using TensorBoards
  • Image classification
  • How to deal with noisy data