• Your selection is empty.

    Register the diplomas, courses or lessons of your choice.

Advanced learning methods

  • School / Prep

    ENSEIRB-MATMECA

Internal code

ET9IA347

Description

This module proposes to study recent systems in deep learning. On the one hand, we'll be looking at recurrent systems, and on the other at non-supervised generative approaches such as antigonistic network methods.

Read more

Teaching hours

  • CMLectures9h
  • TIIndividual work12h
  • PRACTICAL WORKPractical work12h

Syllabus

Recurrent approaches (RNN, LSTM, etc.)
Knowledge transfer approaches
Antagonistic network generative methods (GAN, generator, discriminator, etc.)
Image classification
Object recognition
Super-resolution
Translation in image processing (colorization, style transfer, etc.)

Read more

Further information

Signal and image processing

Read more

Assessment of knowledge

Initial assessment / Main session - Tests

Type of assessmentType of testDuration (in minutes)Number of testsTest coefficientEliminatory mark in the testRemarks
Final inspectionWritten601without document calculator allowed

Second chance / Catch-up session - Tests

Type of assessmentType of testDuration (in minutes)Number of testsTest coefficientEliminatory mark in the testRemarks
Final testWritten601without document calculator allowed