In the world of artificial intelligence, generative models have emerged as a groundbreaking innovation, offering the ability to create new data that mirrors the patterns and characteristics of existing datasets.
From understanding the fundamentals of generative models to exploring popular techniques like Variational Autoencoders (VAEs) and Generative Adversarial Networks (GANs).
We delve into the applications of generative AI across various industries, shaping the future of artificial creativity.
Fundamentals of Generative AI
Generative AI is a fascinating branch of machine learning that seeks to understand the underlying data distribution of a given dataset. Unlike discriminative models that focus on classification tasks, generative models aim to create new data instances that resemble the training data. Key to their functioning are three components: encoders, decoders, and latent spaces.
The Essence of Generative Models: Learning from Data Distribution
Generative models, at their core, learn the statistical representation of the training data. They capture the underlying patterns and dependencies among different features, enabling them to create entirely new data points that share similarities with the original dataset.
Probability and Generative Models: How Probability Shapes AI Creativity
Probability plays a vital role in generative AI, as it helps to quantify the likelihood of different outcomes and events. By modelling the probability distribution of the data, generative models generate samples by sampling from these distributions, resulting in creative and diverse outputs.
Key Components of Generative Models: Encoders, Decoders, and Latent Spaces
Generative models leverage encoders and decoders to transform input data into a meaningful representation (latent space) and then decode it back into a new data point. The latent space serves as the critical bridge between the original data and the generated samples.
Types of Generative AI Techniques
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