How convolution layer work, what is padding, what is recurrent neural network, what is gradient dissent algorithm, etc.

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Convolution layers are used in neural networks for feature extraction, padding is adding zeros to input data, RNNs are used for sequential data, gradient descent is an optimization algorithm.

Convolution layers in neural networks extract features by applying filters to input data.
Padding is adding zeros around the input data to preserve spatial dimensions.
Recurrent Neural Networks (RNNs) are designed to work with sequential data, where the output depends on previous inputs.
Gradient descent is an optimization algorithm used to minimize the loss function by adjusting the model parameters.
Example: Convolution layers in image recognition networks detect edges and textures.
Example: Padding is used in CNNs to maintain the spatial size of the input.
Example: RNNs are used in natural language processing tasks like text generation.
Example: Gradient descent is used in training deep learning models to update weights.

How convolution layer work, what is padding, what is recurrent neural network, what is gradient dissent algorithm, etc.

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