Deep Learning resources for self-study

• http://www.juergenbrauer.org/teaching/deep_learning/slides_ws1617_deep_learning_brauer.pdf
• https://github.com/juebrauer/Book_Introduction_to_Deep_Learning
• http://www.wildml.com/deep-learning-glossary/

1. Biologische Neuronen und technische Neuronenmodelle
2. Convolutional Neural Networks (CNN)
3. R-CNN Modell:
   "Rich feature hierarchies for accurate object detection and semantic segmentation"
   Paper

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4. Fast R-CNN Modell:
   "Fast R-CNN"
   Paper
5. Capsule Networks
   "Dynamic Routing Between Capsules"
   Paper
6. Deep Learning Bibliotheken
   Crashkurs Deep Learning Bibliotheken: TensorFlow und Keras

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7. Faster R-CNN Modell:
   "Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks"
   Paper
8. Reservoir Computing: Echo State Networks
9. Einführung/Crash-Kurs: GPU Programmierung

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10. Yolo und Yolo9000 Modell:
    "You Only Look Once: Unified, Real-Time Object Detection"
    Paper1
    "YOLO9000: Better, Faster, Stronger"
    Paper2
11. Reservoir Computing: Liquid State Machines
12. ILSVRC Benchmark
    Imagenet Large Scale Visual Recognition Challenge (ILSVRC): Wie funktioniert der Wettbewerb?
    Link

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13. SSD Modell:
    "Single Shot MultiBox Detector"
    Paper
14. Generative Adversarial Networks
15. Deep Learning Bibliotheken
    Crashkurs Deep Learning Bibliotheken: Caffe/Caffe2 sowie Torch/PyTorch

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16. Mask R-CNN Modell:
    "Mask R-CNN"
    Paper
17. Neuromorphische Chips