Hidden Layers
In the context of neural networks, hidden layers are the layers of neurons between the input layer and the output layer. These layers are responsible for processing the input data and extracting relevant features that can be used to make predictions or classifications. Each neuron in a hidden layer receives input from the previous layer, applies a non-linear activation function to the weighted sum of its inputs, and passes the output to the next layer. The number of hidden layers and the number of neurons in each layer are hyperparameters that can be tuned to optimize the performance of the neural network. Deep learning models, which are neural networks with multiple hidden layers, have been shown to be highly effective in a wide range of applications, including image recognition, natural language processing, and speech recognition.
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