<ul><li>A groundbreaking paper called 'NoProp' proposes training neural networks without forward or backward propagation.</li><li>NoProp eliminates the traditional steps of forward and backward propagation, reducing memory usage, compute time, and complexity.</li><li>The approach uses weight perturbation with selective feedback and shows potential for training deep networks in new ways.</li><li>NoProp opens doors for alternative training paradigms and challenges conventional deep learning conventions.</li></ul>

NoProp: The Bold Move to Train Neural Networks Without Forward or Backward Propagation

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