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Generate a dimension reducer from a high-dimensional random vector using the autoencoder method:
Reduce new vectors using the trained autoencoder:
Reduce the dimension of some images using the autoencoder method:
Visualize the two-dimensional representation of images:
Scope (1)Create training and test data consisting of two-dimensional numerical sequences of variable length:
Train an autoencoder to find a dense three-dimensional representation of input sequences:
Visualize the similarity between different sequences of different lengths and bounds using the encoder:
Generate new sequences from their encodings:
Options (2) MaxTrainingRounds (1)Obtain the MNIST training dataset:
Train an autoencoder network such that it visits each example exactly once:
NetworkDepth (1)Obtain the MNIST dataset that contains training and test images:
Train several autoencoders with different "NetworkDepth" to reduce the dimensions of the images:
Visualize the two-dimensional representation of images for various network depths:
Applications (2) Data Reconstruction (1)Load the Fashion MNIST training and test dataset:
Train an autoencoder to reduce the dimensions of the images:
Use the reducer to reconstruct images from their encodings and compare with the original images:
Data Visulization (1)Reduce the dimension of some images using the autoencoder method:
Visualize the two-dimensional representation of images:
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