autoencoderscompression
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xxl4tomxu98 / autoencoder-feature-extraction
Use auto encoder feature extraction to facilitate classification model prediction accuracy using gradient boosting models
Language:Jupyter Notebook 11lucylow / CERN_HEP_Autoencoder
High Energy Physics (HEP) autoencoder for CERN ATLAs to compress hadron jet event data from 4 to 3 variables
Language:Jupyter Notebook 8AdityaTheDev / ReconstructionOfImage-Using-DeepAutoEnccoders
Autoencoder is a type of neural network where the output layer has the same dimensionality as the input layer. In simpler words, the number of output units in the output layer is equal to the number of input units in the input layer. An autoencoder replicates the data from the input to the output in an unsupervised manner and is therefore sometimes referred to as a replicator neural network. The autoencoders reconstruct each dimension of the input by passing it through the network. It may seem trivial to use a neural network for the purpose of replicating the input, but during the replication process, the size of the input is reduced into its smaller representation. The middle layers of the neural network have a fewer number of units as compared to that of input or output layers. Therefore, the middle layers hold the reduced representation of the input. The output is reconstructed from this reduced representation of the input.
Language:Jupyter Notebook 1