Stochastic Gradient Descent vs Batch Gradient Descent

Developers should learn SGD when working on machine learning projects involving large datasets, as it reduces memory usage and speeds up training compared to batch gradient descent 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 on machine learning projects involving large datasets, as it reduces memory usage and speeds up training compared to batch gradient descent

Pros

• It is essential for training deep neural networks in frameworks like TensorFlow and PyTorch, and is widely used in applications such as image recognition, natural language processing, and recommendation systems
• Related to: gradient-descent, machine-learning

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.