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Lastly, they are capable of randomly generating new data that looks very similar to the training data; this is called a generative model.
In this chapter we will explain in more depth how autoencoders work, what types of constraints can be imposed, and how to implement them using TensorFlow, whether it is for dimensionality reduction, feature extraction, unsupervised pretraining, or as generative models.
What is a generative model?
• Generative models
A generative model is a model capable of randomly generating outputs that resemble the training instances.
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