Einführung Videos (engl.): https://www.3blue1brown.com/neural-networks

Einführung (Kostenloses Buch, engl.): http://neuralnetworksanddeeplearning.com/

Crash course Google : https://developers.google.com/machine-learning/crash-course/

Machine Learning

Software Links: http://deeplearning.net/software_links/

http://torch.ch/

Object tracking in video with OpenCV and Deep Learning ¹⁾

Pattern: https://developer.ibm.com/patterns/detect-track-and-count-cars-in-a-video/

Repo: https://github.com/IBM/powerai-counting-cars

https://www.tensorflow.org/tutorials

You only look once

Quelle²⁾

You Only Look Once - this object detection algorithm is currently the state of the art, outperforming R-CNN and it's variants. I'll go into some different object detection algorithm improvements over the years, then dive into YOLO theory and a programmatic implementation using Tensorflow!

Code for this video: https://github.com/llSourcell/YOLO_Object_Detection

More learning resources: https://pjreddie.com/darknet/yolo/

https://timebutt.github.io/static/how-to-train-yolov2-to-detect-custom-objects/

http://machinethink.net/blog/object-detection-with-yolo/

https://github.com/pjreddie/darknet/wiki/YOLO:-Real-Time-Object-Detection

https://github.com/KleinYuan/easy-yolo

https://medium.com/@xslittlegrass/almost-real-time-vehicle-detection-using-yolo-da0f016b43de

https://medium.com/diaryofawannapreneur/yolo-you-only-look-once-for-object-detection-explained-6f80ea7aaa1e