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+# Quelle
+https://github.com/bnsreenu/python_for_microscopists/blob/master/260_image_anomaly_detection_using_autoencoders/260_image_anomaly_detection_using_autoencoders.py
+
+``Infos``\
+Detecting anomaly images using AutoEncoders. (Sorting an entire image as either normal or anomaly)\
+Here, we use both the reconstruction error and also the kernel density estimation based on the vectors in the latent space. 
+We will consider the bottleneck layer outputfrom our autoencoder as the latent space.\
+This code uses the malarial data set but it can be easily applied to any application. 
+
+Data from: [Virus für Jonny](https://data.lhncbc.nlm.nih.gov/public/Malaria/cell_images.zip)
+
 # DetectingAnomalies