- Greedy layer-wise pretraining is called greedy as it optimizes each component of the final output network independently.

- Each layer in the final network is optimized independently that is why this pretraining is called layer-wise.

- It is called unsupervised since it uses unsupervised representation learning algorithm.

- Pretraining helps to get a starting point for the weights of the network before actual joint training starts hence the name pretraining.

San Jose State University

- Greedy layer-wise unsupervised pretraining helped researchers  to train a supervised multi-layered deep neural network which did not incorporate complex deep neural network building blocks such  as convolution or recurrence.

- Each layer in a deep neural network is optimized in a greedy manner to obtain an optimized multi layered deep  neural network at the end.

- This type of pretraining requires a single-layer representation learning algorithms such as 
            - RBM( Restricted Boltzmann Machine)
            - a single layer autoencoder
            - a sparse coding model


Greedy Layer-Wise Unsupervised Pretraining

Goodfellow, I., Bengio, Y., & Courville, A. (2016). $\mathit{Deep \ Learning.}$ MIT Press. Retrieved from [www.deeplearningbook.org](https://www.deeplearningbook.org) 

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