Feed-Forward Neural Networks are basic, densely-connected neural networks that connect all of the input nodes to the output nodes without the presence of any loop or recurrent connections. They can learn basic features from the input data and classify them according to patterns in the data.
Convolutional Neural Networks are specialized types of neural networks for image processing. They are capable of extracting features from images that are passed through them, allowing them to recognize certain features from the image such as edges, shapes, and color gradients.
Recurrent Neural Networks are dynamic neural networks used for processing sequence data. They are capable of connecting input data over multiple timesteps and retaining important long-term information. This makes them useful for tasks such as language translation, speech recognition, and time-series analysis.
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