Hidden Layer in Deep Learning Neural Network - Python and Machine Learning for Integrated Circuits - - An Online Book - |
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Python and Machine Learning for Integrated Circuits http://www.globalsino.com/ICs/ | |||||||||||||
Chapter/Index: Introduction | A | B | C | D | E | F | G | H | I | J | K | L | M | N | O | P | Q | R | S | T | U | V | W | X | Y | Z | Appendix | |||||||||||||
================================================================================= In a neural network, the term "hidden layers" refers to the layers that come between the input layer and the output layer. Hidden layers are called "hidden" because they are not directly connected to the input or the final output of the network. These layers play a crucial role in capturing complex, non-linear relationships in the data and extracting high-level features. A typical feedforward neural network consists of the following layers:
The architecture of the hidden layers can vary widely depending on the problem and the design choices made. Each hidden layer applies a linear transformation followed by a non-linear activation function to the data. Common activation functions include ReLU (Rectified Linear Unit), sigmoid, and hyperbolic tangent (tanh). Table 3827. Hidden layer in deep learning neural network.
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