Batch Gradient Descent vs Adam Optimizer

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 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.

Batch Gradient Descent

Nice Pick

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

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. Batch Gradient Descent is a concept while Adam Optimizer is a tool. We picked Batch Gradient Descent based on overall popularity, but your choice depends on what you're building.

The Bottom Line

Batch Gradient Descent wins

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