Convolutional Neural Networks and Image Processing II

Course code: CNNIP

This course is a follow-up of Convolutional Networks and Image Processing in which we will focus on image data preprocessing and advanced techniques of deep learning for image processing. Apart from image classification, well known from the previous course, we will study image segmentation, object detection, and especially advanced applications of generative adversarial networks (GANs) such as superresolution, noise reduction and generating deep fakes.

194 EUR

235 EUR including VAT

The earliest date from 12.10.2022

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Course dates

Starting date: 12.10.2022

Type: Virtual

Course duration: 1 day

Language: cz

Price without VAT: 194 EUR

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Starting date: Individual

Type: Individual

Course duration: 1 day

Language: en

Price without VAT: 194 EUR

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Starting
date
Place
Type Course
duration
Language Price without VAT
12.10.2022 Virtual 1 day cz 194 EUR Register
Individual Individual 1 day en 194 EUR Register
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Course structure

  • Image data preprocessing (number of channels, aspect ratio, image scaling, normalization of image data)
  • Data augmentation (scaling, rotation, shifting, mirroring)
  • Practical example of preprocessing and augmentation
  • DNN architectures for Image Processing (convolution, deconvolution, pooling, residual) connections
  • Advanced image classification (ImageNet dataset, VGG, ResNet, Inception, MobileNet, Efficient net)
  • Practical example of transfer learning using different base models
  • Image Segmentation (U-net, Object detection)
  • Practical example of image segmentation and object detection
  • Generative Adversarial Networks (generative networks, sampling, the adversarial model)
  • Practical example of image generation
  • Image Noise Reduction
  • Practical example of noise reduction using U-net and GANs
  • Superresolution (Upsampling, practical example of using GANs for superresolution)
  • GAN enhancement of datasets
  • Deep fake (Conditional GANs, examples of deep fake techniques)
  • Adversarial attacks (image modification, adversarial patch, black box-attacks, cybersecurity)

Prerequisites

  • 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
  • Knowledge on the level of our basic Convolutional Networks and Image Processing

Do you need advice or a tailor-made course?

daniel

Daniel Šťastný

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