Data Dreamland, Subash Palvel, Data Science, Dimensionality Reduction, Machine Learning.
model learns a compressed representation of the data. The bottleneck layer in the middle of the network acts as a low-dimensional representation of the input. Autoencoders can capture complex patterns and nonlinear relationships in the data, making them suitable for high-dimensional datasets.