Image processing is one of the fields that has benefited most dramatically from advances in Deep Learning.
Topics range from classification and segmentation to image transformation, including the generation of text-oriented analyses.

Technical resources

Course material projected during training and sent to all trainees at the end of the course; case studies and practical examples chosen according to trainees' areas of interest

Performance monitoring

All trainees are asked to sign in every half-day Evaluation: Questionnaire to assess skills acquired at the end of the course

Assessment of results

Post-training satisfaction questionnaire

Pedagogical objectives

Theoretical courses combined with examples and case studies. The aim of this course is to present the main problems encountered in image processing and, for each one, to describe the most effective state-of-the-art solutions.

Technologies covered

Convolutional Neural Nets (CNN), overfitting, regularization, feature maps, VGG, LeNet, Inception, U-Nets,R-CNN, LSTM, CycleGAN, Pix2Pix, Superresolution, denoising, deblurring, colorization, neural style, CRF

Target skills

  • Architectures pour l’image
  • Classification/Detection/Segmentation
  • Image analysis, transformations
  • Interpretation and safety