Master Autoencoders: Essential Guide To Data Representation, Reconstruction, And Applications
Autoencoders are neural networks that learn efficient data representations through an encoder-decoder architecture. The encoder compresses input data into a latent representation, while the decoder reconstructs the input from this representation. Autoencoders are used in applications such as denoising, image compression, and feature extraction. Autoencoders: Unveiling the Secrets of Unsupervised Learning and Data Representation In…