Introduction
Optimization algorithms are the engine that drives deep learning. They determine how neural networks update their weights to minimize loss. Let's explore the evolution from basic SGD to sophisticated adaptive methods like Adam.
Interactive Optimizer Comparison
Select different optimizers to see how they navigate the loss landscape:
SGD
Basic gradient descent with fixed learning rate
Pros
- • Simple
- • Low memory
- • Predictable
Cons
- • Slow convergence
- • Sensitive to learning rate
- • Can get stuck
Learning Rate Schedules
The learning rate is crucial for optimizer performance. Here are common scheduling strategies:
Step Decay
Drops by factor at specific epochs
Exponential Decay
Smoothly decreases over time
Cosine Annealing
Follows cosine curve with restarts
Warmup + Decay
Gradual increase then decay
Choosing the Right Optimizer
| Scenario | Recommended Optimizer | Why |
|---|---|---|
| General deep learning | Adam | Good default, works well out-of-box |
| Computer vision | SGD + Momentum | Often better final accuracy |
| NLP / Transformers | AdamW | Adam with weight decay decoupling |
| Sparse data | AdaGrad | Handles sparse gradients well |
| Online learning | RMSprop | Good for non-stationary objectives |
Recent Advances
🚀 AdamW (2017)
Decouples weight decay from gradient-based optimization, improving generalization.
🎯 LARS/LAMB (2019)
Layer-wise adaptive learning rates for large batch training.
⚡ Lion (2023)
Simpler than Adam with better performance, using sign of gradient momentum.
Conclusion
While Adam remains the go-to optimizer for most tasks, understanding the full spectrum of optimization algorithms helps you make informed choices for your specific use case. Remember: the best optimizer depends on your data, model architecture, and computational constraints.