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 on machine learning projects involving large datasets, as it reduces memory usage and speeds up training compared to batch gradient descent. 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 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

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.