From the course: Deep Learning with Python: Convolutional Neural Networks
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ResNet - Python Tutorial
From the course: Deep Learning with Python: Convolutional Neural Networks
ResNet
- [Instructor] ResNet, short for residual network, is a landmark convolutional neural network architecture introduced by Kaiming He and his team at Microsoft Research in 2015. It significantly advanced the field of deep learning by showing that extremely deep networks could be trained effectively without suffering from the performance degradation that previously came with added depth. Prior to ResNet, adding more layers to a neural network often made the model perform worse rather than better. This was largely due to the vanishing gradient problem, where gradient signals would weaken as they pass backward through many layers, making training difficult. ResNet solved this issue by introducing skip connections, also known as residual connections. These connections bypass one or more layers by adding the input directly to the output of a block of layers. This simple idea allows gradient signals to flow more smoothly through the network, enabling the train of very deep models with 50…
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