#training
6 articles
- Adam Optimizer Adaptive learning rates with momentum for deep learning
- Batch Normalization Normalizing layer inputs to accelerate deep network training
- Dropout: Regularization for Neural Networks Randomly dropping units during training to prevent overfitting
- GPipe: Easy Scaling with Micro-Batch Pipeline Parallelism Training giant neural networks by pipelining micro-batches across devices
- Layer Normalization Normalizing each example across its features
- Pre-training The stage where a model learns broad patterns from a very large dataset