What is Generative Machine Learning?
Generative machine learning involves the development of models that learn the underlying distribution of the training data. These models are capable of generating new data samples, which have similar characteristics to the original dataset. Fundamentally, generative models aim to understand the core of the data in order to generate unique and diverse outputs.
The basic components of generative learning involve appreciation probability distributions, which are used to carry out the process of generating a sample data set. As GANs, VAEs and MCMCs are among the most popular methods that are employed in generative learning.
What is Generative Machine Learning?
Generative Machine Learning is an interesting subset of artificial intelligence, where models are trained to generate new data samples similar to the original training data. In this article, we’ll explore the fundamentals of generative machine learning, compare it with discriminative models, delve into its applications, and conclude with insights into its significance in the AI landscape.