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Some of the examples of RNNs are as follows:
                 • It can generate novel text in the style of a specific author or genre, like creating new sentences that mimic the
                style of Shakespeare or generating dialogue for a chatbot.
                 • It can predict the next character or word in a sequence, like autocomplete features in text editors and predictive
                text input on smartphones.
                 • It can be used to predict future values in a time series, such as stock prices or weather data, by learning patterns
                from historical data.


              Autoencoders (AEs)
              These are neural networks that have been trained to learn a compressed representation of data. They work by
              compressing the data into a lower-dimensional form (encoding) and then decompressing it back to its original
              form (decoding). This process helps the network learn the most important features of the data.






                                                                    Latent Space
                                            Input   Encoder                Decoder         Output









              Some of the examples of AEs are as follows:

                 • It can help in cleaning up noisy images to produce clear and highly realistic samples.
                 • It can help in compressing high-resolution images for efficient storage and transmission.
                 • It can create artistic images based on learned features from famous paintings.
                 • It can help in drug discovery by learning and generating molecular structures that have desirable properties.


              Similarities and Differences between AEs and VAEs

              The similarities and differences between Autoencoders (AEs) and Variational Autoencoders (VAEs) are as follows:

              Similarities
                 • Both AE and VAE are neural network architectures that are used for Unsupervised Learning
                 • Both AE and VAE consist of an encoder and a decoder network. The encoder maps the input data to a latent
                representation, and decoder maps the latent representation back to the original data.
                 • Both AE and VAE can be used for tasks such as dimensionality reduction, data generation, and anomaly detection.

              Differences

                                                        AE                                    VAE

                 Basic Function        Neural network model that learns to   Similar to AE but incorporates
                                       encode input data into a compressed  probabilistic elements to learn a latent
                                       representation and then decode it     space representation of input data.
                                       back to the original data.



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