Stochastic Gradient Descent vs Adam Optimizer

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 meets 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. Here's our take.

Stochastic Gradient Descent

Nice Pick

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

Adam Optimizer

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

The Verdict

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

The Bottom Line

Stochastic Gradient Descent wins

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