Sparse autoencoders, introduced in the 2000s by researchers like Andrew Ng, are neural networks that extract essential features from high-dimensional data while minimising redundancy. They are applied in feature engineering, image compression, and pattern recognition, benefiting industries such as healthcare, finance, and government analytics.
