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  1. Applied Deep Learning 2023 - Lecture 13 - Epilogue - Outlook, Feedback, and GoodbyeApplied Deep Learning 2023 - TU WienNotes
  2. Applied Deep Learning 2023 - Lecture 12 - Explainable AIApplied Deep Learning 2023 - TU WienNotes
  3. Applied Deep Learning 2023 - Lecture 11 - Graph Neural NetworksApplied Deep Learning 2023 - TU WienNotes
  4. Applied Deep Learning 2023 - Lecture 10 - Serving, Optimizing, and Practical AspectsApplied Deep Learning 2023 - TU WienNotes
  5. Applied Deep Learning 2023 - Lecture 9 - Preprocessing, Augmentation, Regularization, VisualizationApplied Deep Learning 2023 - TU WienNotes
  6. Applied Deep Learning 2023 - Lecture 8 - TransformersApplied Deep Learning 2023 - TU WienNotes
  7. Applied Deep Learning 2023 - Lecture 7 - Autoencoders and Generative Adversarial NetworksApplied Deep Learning 2023 - TU WienNotes
  8. Applied Deep Learning 2023 - Lecture 6 - Deep Reinforcement LearningApplied Deep Learning 2023 - TU WienNotes
  9. Applied Deep Learning 2023 - Lecture 5 - Libraries and Practical AspectsApplied Deep Learning 2023 - TU WienNotes
  10. Applied Deep Learning 2023 - Lecture 4 - Recurrent Neural NetworksApplied Deep Learning 2023 - TU WienNotes
  11. Applied Deep Learning 2023 - Lecture 3 - Convolutional Neural Networks and Visual ComputingApplied Deep Learning 2023 - TU WienNotes
  12. Applied Deep Learning 2023 - Lecture 2 - Neural Networks, Optimization and BackpropagationApplied Deep Learning 2023 - TU WienNotes
  13. Stanford CS224N NLP with Deep Learning | 2023 | Lecture 14 - Insights between NLP and LinguisticsStanford CS224N: Natural Language Processing with Deep Learning | 2023Notes
  14. Stanford CS224N NLP with Deep Learning | 2023 | Lecture 11 - Natural Language GenerationStanford CS224N: Natural Language Processing with Deep Learning | 2023Notes
  15. Stanford CS224N | 2023 | Lecture 10 - Prompting, Reinforcement Learning from Human FeedbackStanford CS224N: Natural Language Processing with Deep Learning | 2023Notes
  16. Stanford CS224N NLP with Deep Learning | 2023 | Lecture 9 - PretrainingStanford CS224N: Natural Language Processing with Deep Learning | 2023Notes
  17. Stanford CS224N NLP with Deep Learning | 2023 | Lecture 8 - Self-Attention and TransformersStanford CS224N: Natural Language Processing with Deep Learning | 2023Notes
  18. Applied Deep Learning 2023 - Lecture 1 - Introduction to Deep LearningApplied Deep Learning 2023 - TU WienNotes
  19. Applied Deep Learning 2023 - Lecture 0 - Preliminary InformationApplied Deep Learning 2023 - TU WienNotes
  20. Outlook and Research Insights (Safe, Edge and Meta Reinforcement Learning - Lecture 14, Summer 2023)Reinforcement Learning Course: Lectures (Summer 2023)Notes
  21. Further Contemporary RL Algorithms (TRPO, PPO - Lecture 13, Summer 2023)Reinforcement Learning Course: Lectures (Summer 2023)Notes
  22. Deterministic Policy Gradient Methods (Lecture 12, Summer 2023)Reinforcement Learning Course: Lectures (Summer 2023)Notes
  23. Stochastic Policy Gradient Methods (Lecture 11, Summer 2023)Reinforcement Learning Course: Lectures (Summer 2023)Notes
  24. Value-Based Control with Function Approximation (Lecture 10, Summer 2023)Reinforcement Learning Course: Lectures (Summer 2023)Notes
  25. On-Policy Prediction with Function Approximation (Lecture 09, Summer 2023)Reinforcement Learning Course: Lectures (Summer 2023)Notes
  26. Function Approximation with Supervised Learning (Lecture 08, Summer 2023)Reinforcement Learning Course: Lectures (Summer 2023)Notes
  27. Planning and Learning with Tabular Methods (Lecture 07, Summer 2023)Reinforcement Learning Course: Lectures (Summer 2023)Notes
  28. Multi-Step Bootstrapping (Lecture 06, Summer 2023)Reinforcement Learning Course: Lectures (Summer 2023)Notes
  29. Temporal Difference Learning (Lecture 05, Summer 2023)Reinforcement Learning Course: Lectures (Summer 2023)Notes
  30. Stanford CS330 Deep Multi-Task & Meta Learning - Lifelong Learning I 2022 I Lecture 15Stanford CS330: Deep Multi-Task and Meta Learning I Autumn 2022Notes
  31. Stanford CS330 Deep Multi-Task & Meta Learning - Domain Generalization l 2022 I Lecture 14Stanford CS330: Deep Multi-Task and Meta Learning I Autumn 2022Notes
  32. Stanford CS330 Deep Multi-Task & Meta Learning - Domain Adaptation l 2022 I Lecture 13Stanford CS330: Deep Multi-Task and Meta Learning I Autumn 2022Notes
  33. Stanford CS330 Deep Multi-Task & Meta Learning - Bayesian Meta-Learning l 2022 I Lecture 12Stanford CS330: Deep Multi-Task and Meta Learning I Autumn 2022Notes
  34. Stanford CS330 I Variational Inference and Generative Models l 2022 I Lecture 11Stanford CS330: Deep Multi-Task and Meta Learning I Autumn 2022Notes
  35. Stanford CS330 I Advanced Meta-Learning 2: Large-Scale Meta-Optimization l 2022 I Lecture 10Stanford CS330: Deep Multi-Task and Meta Learning I Autumn 2022Notes
  36. Stanford CS330 I Advanced Meta-Learning TopicsTask Construction l 2022 I Lecture 9Stanford CS330: Deep Multi-Task and Meta Learning I Autumn 2022Notes
  37. Stanford CS330 I Unsupervised Pre-training for Few-shot Learning l 2022 I Lecture 8Stanford CS330: Deep Multi-Task and Meta Learning I Autumn 2022Notes
  38. Stanford CS330 I Unsupervised Pre-Training:Contrastive Learning l 2022 I Lecture 7Stanford CS330: Deep Multi-Task and Meta Learning I Autumn 2022Notes
  39. Stanford CS330 Deep Multi-Task & Meta Learning - Non-Parametric Few-Shot Learning l 2022 I Lecture 6Stanford CS330: Deep Multi-Task and Meta Learning I Autumn 2022Notes
  40. Group Equivariant Deep Learning - Lecture 3.1: Motivation for SE(3) equivariant graph NNsGroup Equivariant Deep Learning (UvA - 2022)Notes
  41. Group Equivariant Deep Learning - Lecture 2.7: Derivation of Harmonic Networks from Regular G-ConvsGroup Equivariant Deep Learning (UvA - 2022)Notes
  42. Group Equivariant Deep Learning - Lecture 2.6: Activation Functions for Steerable G-CNNsGroup Equivariant Deep Learning (UvA - 2022)Notes
  43. Group Equivariant Deep Learning - Lecture 2.5: Steerable group convolutionsGroup Equivariant Deep Learning (UvA - 2022)Notes
  44. Group Equivariant Deep Learning - Lecture 2.4: Group Theory (Induced representation, feature fields)Group Equivariant Deep Learning (UvA - 2022)Notes
  45. Group Equivariant Deep Learning - Lecture 2.3: Group Theory (Irreducible representations, Fourier)Group Equivariant Deep Learning (UvA - 2022)Notes
  46. Group Equivariant Deep Learning - Lecture 2.2: Revisiting Regular G-Convs with Steerable KernelsGroup Equivariant Deep Learning (UvA - 2022)Notes
  47. Group Equivariant Deep Learning - Lecture 2.1: Steerable kernels/basis functionsGroup Equivariant Deep Learning (UvA - 2022)Notes
  48. Group Equivariant Deep Learning - Lecture 1.1: IntroductionGroup Equivariant Deep Learning (UvA - 2022)Notes
  49. Group Equivariant Deep Learning - Lecture 1.2: Group theory (product, inverse, representations)Group Equivariant Deep Learning (UvA - 2022)Notes
  50. Stanford CS224N NLP with Deep Learning | Winter 2021 | Lecture 15 - Add Knowledge to Language ModelsStanford CS224N: Natural Language Processing with Deep Learning | 2023Notes

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