The Residual Network (ResNet) Standard: Unterschied zwischen den Versionen
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Die Seite wurde neu angelegt: „ResNet, also called Residual Network, is a very important model in deep learning history. Before ResNet, training very deep neural networks was really hard because of the vanishing gradient problem. This means the signal used to update the weights become very small as it move backward through many layers. ResNet fixed this problem by adding skip connections, also known as shortcuts. These connections let the gradient flow around some layers instead of pa…“ |
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ResNet, also called Residual Network, is a very important model in deep learning history. Before ResNet, training very deep neural networks was really hard because of the vanishing gradient problem. This means the signal used to update the weights | ResNet, also called Residual Network, is a very important model in deep learning history. Before ResNet, training very deep neural networks was really hard because of the vanishing gradient problem. This means the signal used to update the weights becomes very small as it moves backward through many layers. | ||
ResNet fixed this problem by adding skip connections, also known as shortcuts. These connections let the gradient flow around some layers instead of passing through all of them. Because of this, very deep networks can be trained, even with hundreds of layers, like ResNet-50, ResNet-101, and ResNet-152. | ResNet fixed this problem by adding skip connections, also known as shortcuts. These connections let the gradient flow around some layers instead of passing through all of them. Because of this, very deep networks can be trained, even with hundreds of layers, like ResNet-50, ResNet-101, and ResNet-152.<ref>MathWorks. ''Train Residual Network for Image Classification''. [Online]. Available at: https://www.mathworks.com/help/deeplearning/ug/train-residual-network-for-image-classification.html</ref> | ||
== References == | |||
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Version vom 10. Februar 2026, 15:56 Uhr
ResNet, also called Residual Network, is a very important model in deep learning history. Before ResNet, training very deep neural networks was really hard because of the vanishing gradient problem. This means the signal used to update the weights becomes very small as it moves backward through many layers.
ResNet fixed this problem by adding skip connections, also known as shortcuts. These connections let the gradient flow around some layers instead of passing through all of them. Because of this, very deep networks can be trained, even with hundreds of layers, like ResNet-50, ResNet-101, and ResNet-152.[1]
References
- ↑ MathWorks. Train Residual Network for Image Classification. [Online]. Available at: https://www.mathworks.com/help/deeplearning/ug/train-residual-network-for-image-classification.html