Generative AI is a type of artificial intelligence technology that can produce various types of content, including text, imagery, audio and synthetic data.
Generative AI techniques use deep learning neural networks, which are trained on large amounts of data. The neural networks can then generate new data based on the patterns they have learned from the training data.
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Generative Adversarial Networks (GANs): GANs are a type of generative AI technique that use two neural networks, a generator and a discriminator, that work together to produce new content. The generator creates new content, and the discriminator evaluates the content to determine whether it is real or fake. The generator then uses the discriminator’s feedback to improve its ability to produce realistic content. GANs can be used to create a wide range of content, including images, text, audio and video.
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Variational Autoencoders (VAEs): VAEs are a type of generative AI technique that use neural networks to encode data into a lower-dimensional latent space, and then decode the data back from the latent space into its original form. VAEs can be used to create new content based on the patterns they have learned from the training data.
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Autoregressive Models: Autoregressive models are a type of generative AI technique that use neural networks to predict the next value in a sequence of data, such as a sequence of words in a sentence or a sequence of pixels in an image. Autoregressive models can be used to create new content based on the patterns they have learned from the training data.
- Markov Chain Text Generator
- GANS
- Autoencoders
- Variational Autoencoders
- Autoregressive Models