Title: Autoencoders -Machine Learning
Authors: Bexzod Norboyev
Volume: 8
Issue: 10
Pages: 13-20
Publication Date: 2024/10/28
Abstract:
At the heart of deep learning lies the neural network, an intricate interconnected system of nodes that mimics the human brain's neural architecture. Neural networks excel at discerning intricate patterns and representations within vast datasets, allowing them to make predictions, classify information, and generate novel insights. Autoencoders emerge as a fascinating subset of neural networks, offering a unique approach to unsupervised learning. Autoencoders are an adaptable and strong class of architectures for the dynamic field of deep learning, where neural networks develop constantly to identify complicated patterns and representations. With their ability to learn effective representations of data, these unsupervised learning models have received considerable attention and are useful in a wide variety of areas, from image processing to anomaly detection.