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

Quantum Computing and AI are two fields that can complement and enhance each other. Quantum Computing can perform calculations faster and more efficiently than…
Predictive Analytics uses historical data and statistical modelling techniques to make predictions about future outcomes. By analysing patterns and trends within data, businesses can…
Support Vector Machines (SVMs) are Machine Learning algorithms used for classification and regression tasks. SVMs create decision boundaries and maximise the margin between different…