Module 7: Training Tricks – Make Your Models Learn Better & Faster

Duration: Week 14
Difficulty: Intermediate
Prerequisites: Modules 1-6 completed

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🎯 What You’ll Learn

Professional tricks to train models faster, use less memory, and get better results!

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📈 Part 1: Learning Rate Scheduling

The Problem

• High learning rate: Fast but unstable
• Low learning rate: Stable but slow

Solution: Change learning rate during training!

Cosine Annealing (Most popular):

Start: High LR (0.01) → Learn fast
Middle: Gradually decrease → Refine
End: Very low (0.0001) → Fine-tune

Analogy: Driving to a destination

• Highway: Fast (high LR)
• City streets: Slower (medium LR)
• Parking: Very slow (low LR)

Project: Implement Schedulers

• Code cosine annealing from scratch
• Try: Step decay, exponential decay
• Result: 5-10% better accuracy!

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💾 Part 2: Mixed Precision – Use Less Memory

The Idea

Not all numbers need 32 bits of precision!

Strategy:

• Most calculations: 16-bit (FP16)
• Final results: 32-bit (FP32)

Benefits:

• 2x less memory
• 2x faster training
• Almost no accuracy loss!

Project: Add Mixed Precision

• Modify training code
• Add automatic mixed precision (AMP)
• Benchmark: Speed and memory savings

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🔧 Part 3: Hyperparameter Tuning

Hyperparameters to Tune

• Learning rate (most important!)
• Batch size
• Number of layers
• Hidden dimensions
• Dropout rate

Automated Tuning with Optuna

Optuna tries different combinations smartly
Finds optimal settings in 50-100 trials

Project: Hyperparameter Search

• Use Optuna library
• Define search space
• Run 50 trials overnight
• Result: 3-5% accuracy improvement!

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🎓 Part 4: Smart Training Techniques

1. Early Stopping

Monitor validation loss
If no improvement for 10 epochs → STOP
Save best model, not final model

2. Gradient Accumulation

Process 16 samples → Save gradients
Process 16 more → Add to gradients
Repeat 4 times
Update weights (effective batch size = 64!)

3. Automatic Batch Size Finder

def find_max_batch_size():
    batch_size = 1
    while fits_in_memory(batch_size):
        batch_size *= 2
    return batch_size // 2

4. Model Checkpointing

• Save model every epoch
• If training crashes → Resume
• Keep best model based on validation

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⚡ ISL Optimization

1. CPU Optimization

• Use Intel MKL (Math Kernel Library)
• Enable OpenBLAS
• Result: 2-3x faster on CPU!

2. Efficient Data Loading

Bad: Load → Process → Train → Load next
Good: While training, load next batch in background

3. Memory Profiling

• Use memory_profiler
• Track RAM usage over time
• Find and fix leaks

4. Gradient Checkpointing

• Trade compute for memory
• Don’t save all activations
• Result: 10x less memory, 30% slower

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📚 Resources

• Optuna documentation
• PyTorch Lightning
• Weights & Biases (free tier)

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✅ Learning Checklist

• [ ] Implement learning rate schedules
• [ ] Use mixed precision training
• [ ] Automatically find best hyperparameters
• [ ] Apply early stopping
• [ ] Optimize data loading
• [ ] Use gradient checkpointing

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🚀 Next Steps

Module 8: Deploy Your AI

Turn your models into real applications!