Abschnittsübersicht

◄Exercises

  • 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)

◄Exercises