Section outline

• Välj avsnitt Allmänt

  Allmänt

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  Lectures: On Mondays, 12-14

  Exercises: On Tuesdays, 14-16

  Location (both lectures and exercises): Seminarraum 2 in the new KI Gebäude, ground floor (Campus Nord, Emil-Fischer-Str. 50)     

  • Välj aktivitet Ankündigungen

    

    Ankündigungen Forum
• Välj avsnitt Mock Exam

  Mock Exam

  Tillgänglig om: Fältet E-postadress är inte tomt
• Välj avsnitt Lectures

  Lectures

  • Välj aktivitet Lecture 1: Intro to DL + Course Organization

    

    Lecture 1: Intro to DL + Course Organization Fil
  • Välj aktivitet Lecture 2: Feed Forward Networks

    

    Lecture 2: Feed Forward Networks Fil
  • Välj aktivitet Lecture 3: Optimization & Training

    

    Lecture 3: Optimization & Training Fil
  • Välj aktivitet Lecture 4: Convolutional Neural Networks

    

    Lecture 4: Convolutional Neural Networks Fil

    Please find the PDF & the PPT here, as well as the video recording for the second part of the lecture on important CNN architectures.

    The .pptx slides contain additional videos / animations that are not playing in the PDF.

     

     

  • Välj aktivitet Lecture 5: Autoencoders and Generative Adversarial Networks

    

    Lecture 5: Autoencoders and Generative Adversarial Networks Fil
  • Välj aktivitet Lecture 6: Recurrent Neural Networks, Sequence to Sequence and Attention Models

    

    Lecture 6: Recurrent Neural Networks, Sequence to Sequence and Attention Models Fil
  • Välj aktivitet Lecture 7: Attention & Transformers

    

    Lecture 7: Attention & Transformers Fil
  • Välj aktivitet Lecture 8 - Introduction to Reinforcement Learning

    

    Lecture 8 - Introduction to Reinforcement Learning Fil
  • Välj aktivitet Lecture 9 - Deep Reinforcement Learning

    

    Lecture 9 - Deep Reinforcement Learning Fil
  • Välj aktivitet Lecture 10 - Graph Representation Learning

    

    Lecture 10 - Graph Representation Learning Fil
  • Välj aktivitet Lecture 11 - Graph Neural Networks

    

    Lecture 11 - Graph Neural Networks Fil
• Välj avsnitt Exercises

  Exercises

  • Välj aktivitet Exercises

    

    Exercises Forum

    If you have questions or problems with the exercises, you can ask here for help. 

  • Välj aktivitet Exercise 1

    

    Exercise 1 Inlämningsuppgift

    Exercise for Lecture 2

  • Välj aktivitet Exercise 2

    

    Exercise 2 Inlämningsuppgift
  • Välj aktivitet Exercise 3

    

    Exercise 3 Inlämningsuppgift
  • Välj aktivitet Exercise 4

    

    Exercise 4 Inlämningsuppgift
  • Välj aktivitet Exercise 5

    

    Exercise 5 Inlämningsuppgift
  • Välj aktivitet Exercise 6

    

    Exercise 6 Inlämningsuppgift
  • Välj aktivitet Exercise 7

    

    Exercise 7 Inlämningsuppgift
  • Välj aktivitet Exercise 8

    

    Exercise 8 Inlämningsuppgift
  • Välj aktivitet Exercise 9

    

    Exercise 9 Inlämningsuppgift
  • Välj aktivitet Exercise 10

    

    Exercise 10 Inlämningsuppgift
  • Välj aktivitet Exam review / Klausureinsicht

    

    Exam review / Klausureinsicht Tidsbokning
  • Välj aktivitet Feedback DL

    

    Feedback DL Återkoppling
• Välj avsnitt Content & schedule (planned, changes still possible)

  Content & schedule (planned, changes still possible)

  15.4. L1: Introduction to Deep Learning & Course Organization (Dozent: Goran Glavaš)

  22.4. L2: Building Blocks & Feed-forward Nets (Dozentin: Katharina Breininger)

  29.4. L3:  Optimization & Training (Dozent: Goran Glavaš)

  13.5. L4: Convolutional networks (Dozentin: Katharina Breininger)

  27.5. L5: Autoencoders & Generative Adversarial Networks (Dozent/in: Radu Timofte / Nancy Mehta )

  3.6. L6: Recurrent Networks (Dozent: Andreas Hotho)

  10.6. L7: Attention & Transformers (Dozent: Andreas Hotho)

  17.6. L8: Intro to Reinforcement Learning (Dozent: Carlo D'Eramo)

  24.6. L9: Deep Reinforcement Learning (Dozent: Carlo D'Eramo)

  1.7. L10: Graph Representation Learning (Dozent: Ingo Scholtes)

  8.7. L11: Neural Networks for/on Graphs (Dozent: Ingo Scholtes)