<ul><li>Backpropagation requires retaining intermediate activations and gradients, resulting in high memory usage.</li><li>To optimize deep learning for large models, several memory-efficient techniques can be adopted.</li><li>These techniques include gradient checkpointing, mixed precision training, reversible architectures, low-rank gradient compression, and ZeRO optimization.</li><li>By implementing these strategies, researchers and engineers can train deep learning models at scale while minimizing memory consumption.</li></ul>

Memory-Efficient Backpropagation: Optimizing Deep Learning for Large Models

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