Autoencoders and unsupervised learning

Up until now, we discussed neural networks with multiple layers and a wide variety of parameters to optimize. The current generation of neural networks that we often refer to as deep learning is capable of more; it is capable of learning new features automatically so that very little feature engineering and domain expertise is required. These features are created by unsupervised methods on unlabeled data later to be fed into a subsequent layer in a neural network. This method is referred to as (unsupervised) pretraining. This approach has been proven to be highly successful in image recognition, language learning, and even vanilla machine learning projects. The most important and dominant technique in recent ...

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