Training Optimization

➺ Core Concepts:

  • Batch size selection
  • Learning rate scheduling
  • Gradient accumulation
  • Distributed training 

    • ➺ Implementation Strategies:

    Batch Size Optimization 
  • Dynamic batch sizing
  • Gradient accumulation for limited memory
  • Memory vs. speed tradeoffs 
    • Learning Rate Strategies 
  •  Linear scaling rule
  • Warm-up periods
  • Cyclical learning rates
  • One-cycle policy 
    • Distributed Training 
  • Data parallelism
  • Model parallelism
  • Pipeline parallelism
  • Sharded training 

    • ➺ Advanced Techniques:

  • Gradient clipping
  • Loss scaling
  • Adaptive optimizers
  • Knowledge distillation

    • ➺ Monitoring & Metrics:

  • Training curves analysis
  • Resource utilization
  • Convergence indicators
  • Performance bottlenecks