Batch normalization is a widely used technique for making neural-network training faster and more stable. It normalizes intermediate activations and then applies learned scale and shift parameters, allowing the model to keep the representation it needs. Normalization inside the network During training, batch normalization calculates a mean and variance from the current mini-batch. Activations are…
Tag: Neural Networks
Why Residual Connections Make Deep Networks Easier to Train
Adding more layers should give a neural network more capacity, but very deep networks can become harder to optimize. Residual connections, popularized by ResNet, solve an important part of this problem and allow hundreds of layers to be trained effectively. The degradation problem A deeper model does not always achieve lower training error than a…