What is Variational Autoencoders (VAE)?

Skill Level:

Variational Autoencoders are a type of generative model used in unsupervised learning. VAEs learn a low-dimensional representation of input data and can generate new data samples similar to the training data. They have applications in tasks such as image generation, anomaly detection, and data compression.

Other Definitions

Fuzzy Logic is a mathematical framework that deals with uncertainty and imprecision. By assigning degrees of truth to statements, Fuzzy Logic allows businesses to…
Synthetic Data is artificially generated data that mimics real-world data. Synthetic data can be used to train Machine Learning models when real data is…
Federated Learning is a privacy-preserving technique where AI models are trained across multiple decentralised devices or systems without sharing raw data. Instead, only aggregated…
The Viterbi Algorithm is a dynamic programming algorithm used in sequence analysis, such as speech recognition and Natural Language Processing. It finds the most…