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Types of Generative AI


              Generative AI comes in a variety of forms, each with unique advantages and uses. Some of the most typical
              varieties are as follows:
                 • Generative Adversarial Networks (GANs)

                 • Variational Autoencoders (VAEs)
                 • Recurrent Neural Networks (RNNs)
                 • Autoencoders (AEs)

              Let's study about them in detail.


              Generative Adversarial Networks (GANs)

              GANs are neural networks that work to produce fresh data. It is made up of two neural networks, which work
              together in a unique adversarial process to create realistic synthetic data. These two neural networks are as
              follows:
                 • Generator Network: It produces data that is as close as possible to real data.
                 • Discriminator Network: It analyses data and provides feedback, i.e. it takes real data and the data generated
                by the generator as input and attempts to distinguish between the two.

              These two networks work together in a cycle where the generator tries to create realistic fake data, and the
              discriminator tries to identify whether the data is real or fake. This back-and-forth process helps the generator
              improve and produce more convincing data over time.










                                                     Generator        Discriminator
                                                     network:           network:
                                                    produces the       analyses the
                                                       data          data and shares
                                                                        feedback









              Some of the examples of GANs are as follows:
                 • It can create portraits of non-existing people.

                 • It can convert images from day to night.
                 • It can generate images based on textual description, for example, if we give a description of a bird, then it will
                create an image that is similar to the description.
                 • It can generate realistic video, which can be used in film production, video games, and generating synthetic data
                for training other AI models, etc.

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