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Diagnosing Neural Network Instability
An engineer is training a deep neural network and observes that the raw output values from many neurons (the weighted sum of their inputs) are becoming extremely large, leading to an unstable training process where the model fails to learn effectively. To solve this, a specific type of mathematical function is typically applied to the raw output of each neuron before it is passed to the next layer.
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Role of the Activation Functions in Neural Networks
How to Select an Activation Function
A neural network with multiple hidden layers is designed so that for every neuron, its output is simply the direct weighted sum of its inputs. No further mathematical transformation is applied to this sum before it is passed to the next layer. What is the most significant consequence of this design on the network's overall capability?
Diagnosing Neural Network Instability
Impact of Linearity in a Multi-Layer Network