Adam Optimizer vs Stochastic Gradient Descent

Developers should learn and use Adam Optimizer when training deep neural networks, especially in scenarios involving large datasets or complex models like convolutional neural networks (CNNs) or transformers meets developers should learn sgd when working with large-scale machine learning problems, such as training deep neural networks on massive datasets, where computing the full gradient over all data points is computationally prohibitive. Here's our take.

Adam Optimizer

Nice Pick

Developers should learn and use Adam Optimizer when training deep neural networks, especially in scenarios involving large datasets or complex models like convolutional neural networks (CNNs) or transformers

Pros

• It is particularly effective for non-stationary objectives and problems with noisy or sparse gradients, such as natural language processing or computer vision tasks, as it automatically adjusts learning rates and converges faster than many other optimizers
• Related to: stochastic-gradient-descent, deep-learning

Cons

• Specific tradeoffs depend on your use case

Stochastic Gradient Descent

Developers should learn SGD when working with large-scale machine learning problems, such as training deep neural networks on massive datasets, where computing the full gradient over all data points is computationally prohibitive

Pros

• It is particularly useful in online learning scenarios where data arrives in streams, and models need to be updated incrementally
• Related to: gradient-descent, optimization-algorithms

Cons

• Specific tradeoffs depend on your use case

The Verdict

These tools serve different purposes. Adam Optimizer is a tool while Stochastic Gradient Descent is a methodology. We picked Adam Optimizer based on overall popularity, but your choice depends on what you're building.

The Bottom Line

Adam Optimizer wins

Based on overall popularity. Adam Optimizer is more widely used, but Stochastic Gradient Descent excels in its own space.