Deep Learning

Core Principles

• Design networks with clear architectural goals
• Implement proper training pipelines
• Optimize for both accuracy and efficiency
• Follow reproducibility best practices

Network Architecture

Layer Design

• Choose appropriate layer types for the task
• Implement proper normalization (BatchNorm, LayerNorm)
• Use activation functions appropriately
• Design skip connections when beneficial

Model Structure

• Start simple, add complexity as needed
• Use modular, reusable components
• Implement proper initialization
• Consider computational constraints

Training Strategies

Optimization

• Choose appropriate optimizers (Adam, SGD, AdamW)
• Implement learning rate schedules
• Use gradient clipping for stability
• Apply weight decay for regularization

Data Handling

• Implement efficient data pipelines
• Apply appropriate augmentations
• Handle class imbalance properly
• Use proper validation strategies

Multi-GPU Training

DataParallel

• Use for simple multi-GPU setups
• Understand synchronization overhead
• Handle batch size scaling

DistributedDataParallel

• Implement for large-scale training
• Handle gradient synchronization
• Manage process groups properly
• Scale learning rates appropriately

Memory Optimization

Gradient Accumulation

• Simulate larger batch sizes
• Handle loss scaling properly
• Implement proper gradient synchronization

Mixed Precision

• Use

  torch.cuda.amp

  or equivalent
• Handle loss scaling for stability
• Choose appropriate precision for operations

Checkpointing

• Trade compute for memory
• Implement activation checkpointing
• Choose checkpoint granularity wisely

Evaluation and Debugging

• Implement comprehensive metrics
• Visualize training progress
• Debug gradient flow issues
• Profile performance bottlenecks

Best Practices

• Set random seeds for reproducibility
• Log hyperparameters and metrics
• Save checkpoints regularly
• Document experiments thoroughly