You Don’t Need Backpropagation To Train Neural Networks Anymore
SMRTR summary
Researchers from the University of Oxford have developed NoProp, a new algorithm for training neural networks without backpropagation. NoProp eliminates the need for forward passes and trains each layer independently, potentially reducing memory usage and enabling better parallelization compared to traditional methods.
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