Deep Learning(CS7015): Lec 7.1 Introduction to Autoncoders

Summary

lec07mod01

Autoencoders are a type of feed-forward neural network used for unsupervised learning tasks such as dimensionality reduction and data compression. They consist of an encoder and a decoder, with the encoder transforming the input into a hidden representation and the decoder reconstructing the original input. The dimensions of the hidden representation can be either undercomplete or overcomplete, depending on whether they are smaller or larger than the input dimensions. The choice of activation functions and loss functions in autoencoders depends on the nature of the input, with options such as sigmoid, linear, and logistic functions. The loss function is typically the squared error loss for real-valued inputs and the cross entropy loss for binary inputs. Autoencoders can be trained using backpropagation, reusing computations and code from previous lectures on backpropagation.

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