Stochastic Gradient Descent vs Batch 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 meets developers should learn batch gradient descent when working on supervised learning tasks where the training dataset is small to moderate in size, as it guarantees convergence to the global minimum for convex functions. 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

Batch Gradient Descent

Developers should learn Batch Gradient Descent when working on supervised learning tasks where the training dataset is small to moderate in size, as it guarantees convergence to the global minimum for convex functions

Pros

• It is particularly useful in scenarios requiring precise parameter updates, such as in academic research or when implementing algorithms from scratch to understand underlying mechanics
• Related to: stochastic-gradient-descent, mini-batch-gradient-descent

Cons

• Specific tradeoffs depend on your use case

The Verdict

These tools serve different purposes. Stochastic Gradient Descent is a methodology while Batch Gradient Descent is a concept. 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 Batch Gradient Descent excels in its own space.