01Model architecture analysis to identify training bottlenecks
02Training efficiency metrics evaluation for accuracy and resource usage
03Regularization strategy application including L1/L2 and dropout
04Intelligent optimizer selection between Adam, SGD, and momentum-based methods
05Automated implementation of learning rate scheduling and decay
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