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* https://medium.com/technology-invention-and-more/how-to-build-a-simple-neural-network-in-9-lines-of-python-code-cc8f23647ca1#.qvxmhqeuu | * https://medium.com/technology-invention-and-more/how-to-build-a-simple-neural-network-in-9-lines-of-python-code-cc8f23647ca1#.qvxmhqeuu | ||
* http://iamtrask.github.io/2015/07/12/basic-python-network/ | * http://iamtrask.github.io/2015/07/12/basic-python-network/ | ||
| + | * https://appliedgo.net/perceptron/ | ||
==Deep Learning== | ==Deep Learning== | ||
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* '''Coco Dataset''' http://cocodataset.org/#explore | * '''Coco Dataset''' http://cocodataset.org/#explore | ||
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| + | * '''CelebA''' http://mmlab.ie.cuhk.edu.hk/projects/CelebA.html (ca. 200.000 Bilder von ca. 10.000 Celebs) | ||
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| + | * '''VoxCeleb''' http://www.robots.ox.ac.uk/~vgg/data/voxceleb/ (VoxCeleb is an audio-visual dataset consisting of short clips of human speech, extracted from interview videos uploaded to YouTube) | ||
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| + | * '''100.000 generated faces''' https://generated.photos/ | ||
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| + | * '''Diverse Kulturdaten''' https://codingdavinci.de/daten/ | ||
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[[Category:Datasets]] | [[Category:Datasets]] | ||
Aktuelle Version vom 27. November 2019, 21:03 Uhr
Das Perceptron
- https://en.wikipedia.org/wiki/Perceptron
- https://medium.com/technology-invention-and-more/how-to-build-a-simple-neural-network-in-9-lines-of-python-code-cc8f23647ca1#.qvxmhqeuu
- http://iamtrask.github.io/2015/07/12/basic-python-network/
- https://appliedgo.net/perceptron/
Deep Learning
- Codes zu Osinga: Deep Learning Cookbook - https://github.com/DOsinga/deep_learning_cookbook
- Codes zu Chollet: Deep Learning mit Python und Keras - https://github.com/keras-team/keras/tree/master/examples
- Codes zu Géron: Machine Learning mit Scikit-Learn & TensorFlow - https://github.com/ageron/handson-ml
- Ian Goodfellow and Yoshua Bengio and Aaron Courville: Deep Learning (Book) - https://www.deeplearningbook.org/
- Francois Chollet, »Deep Learning with Python«
- Github Repositrory: https://github.com/fchollet/deep-learning-with-python-notebooks
- Tariq Rashid, »Neuronale Netze selbst programmieren«
- Github Repository: https://github.com/makeyourownneuralnetwork/makeyourownneuralnetwork
Anwendungen, Datasets etc.
- deepL AI Assistance for Language https://www.deepl.com/translator
- Deep Speech 0.2.0 https://github.com/mozilla/DeepSpeech/releases/tag/v0.2.0
- Darknet Yolo - schnelle Objekterkennung https://pjreddie.com/darknet/yolo/
- Fork von Darknet Yolo (mit mehr Anwendungen und besser erklärt) https://github.com/AlexeyAB/darknet
- Coco Dataset http://cocodataset.org/#explore
- CelebA http://mmlab.ie.cuhk.edu.hk/projects/CelebA.html (ca. 200.000 Bilder von ca. 10.000 Celebs)
- VoxCeleb http://www.robots.ox.ac.uk/~vgg/data/voxceleb/ (VoxCeleb is an audio-visual dataset consisting of short clips of human speech, extracted from interview videos uploaded to YouTube)
- 100.000 generated faces https://generated.photos/
- Diverse Kulturdaten https://codingdavinci.de/daten/