Layers

In the context of data science and artificial intelligence, layers refer to the building blocks of neural networks. A neural network is composed of multiple layers, each of which performs a specific function in the overall computation. The input layer receives the raw data, which is then processed by one or more hidden layers, and finally, the output layer produces the final result. Each layer consists of a set of neurons, which are connected to the neurons in the adjacent layers. The connections between the neurons are weighted, and the weights are adjusted during the training process to optimize the performance of the network. The number of layers and the number of neurons in each layer are hyperparameters that can be tuned to improve the accuracy of the model.
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