The simplest type of neural network where connections between the nodes do not form cycles. Information moves in one direction—from the input layer through the hidden layers to the output layer. To be continued…
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Recurrent Neural Networks
Designed for sequential data, these networks have connections that loop back on themselves, allowing them to maintain memory of previous inputs. This makes them ideal for tasks like language modeling and time series prediction. To be continued …
Generative Adversarial Networks
Consisting of two networks (generator and discriminator) that contest with each other, GANs are used for generating new data instances that resemble the training data. To be continued …