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Deep Learning 101
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For MLPs and backprop and perhaps simple perceptrons how is the change in the weights determined?

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1. For MLPs and backprop and perhaps simple perceptrons how is the change in the weights determined?

It determines the proximity of the output of the network to the desired state.
An artificial neural network is composed of many artificial neurons that are linked together according to a specific network architecture. The objective of the neural network is to transform the inputs into meaningful outputs.
Determined by the partial differential of the cost function with respect to the weight vector.
- learning by adaptation - at the neural level the learning happens by changing of the synaptic strengths - eliminating some synapses and building new ones - synchronous activation increases the synaptic strength - asynchronous activation decreases the synaptic strenth

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For MLPs and backprop and perhaps simple perceptrons how is the change in the weights determined?

❤ If you liked Fatskills, consider supporting us by checking out The Life Manuals You Never Got.

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All trademarks, logos and brand names are the property of their respective owners. All company, product and service names used in this website are for identification purposes only. Use of these names, trademarks and brands does not imply endorsement.

About | Explore | User Guide | Topics | Subjects | Doubt Solver | Career Aptitude Test | Answers | Free Tools | What Should We Know?
Privacy | Terms |

Without work one finishes nothing. - Ralph Waldo Emerson
© 2026 Fatskills.com