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Impact of Linearity in a Multi-Layer Network
A data scientist is building a neural network with several hidden layers to classify images. They consider two approaches for the hidden layers. In Approach A, the output of each neuron is the direct weighted sum of its inputs. In Approach B, a non-linear mathematical function is applied to the weighted sum before passing the result to the next layer. Which approach is fundamentally more capable of learning the complex patterns in the images, and why? Explain the critical limitation of the less capable approach.
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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?
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Impact of Linearity in a Multi-Layer Network