As far as I know, both autoencoders and t-SNE are used for nonlinear dimensionality reduction. What are the differences between them and why should I use one versus another?

**Answer**

Both of them try to find a lower dimensionality embedding of your data. However, there are different minimization problems. More specifically, an autoencoder tries to minimize the reconstruction error, while t-SNE tries to find a lower dimensional space and at the same time it tries to preserve the neighborhood distances. As a result of this attribute, t-SNE is usually preferred for plots and visualizations.

**Attribution***Source : Link , Question Author : RockTheStar , Answer Author : Yannis Assael*