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There is no optimizer that is best for every model. Adam is a strong candidate when gradients are noisy or sparse and can make early training progress quickly; SGD, often with momentum, deserves a fair comparison when held-out performance matters. Choose by comparing validation results under a controlled training setup—not by training loss alone.
SGD vs. Adam: what changes in the update?
Ordinary stochastic gradient descent (SGD) updates parameters using the current gradient scaled by a learning rate. It does not adapt each parameter’s update scale using gradient-moment estimates. SGD with momentum also accumulates update direction over time, but it is still distinct from Adam’s per-parameter adaptive scaling.
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Adam keeps exponential moving averages of gradients and squared gradients. It corrects those estimates for initialization bias, then uses the corrected first moment divided by the square root of the corrected second moment plus a small epsilon. The result is an update scale that adapts parameter by parameter based on gradient history. See the algorithm in Kingma and Ba’s original Adam paper.
When should you use Adam instead of SGD?
Adam is a reasonable first candidate when your optimization problem has very noisy or sparse gradients, or changes over time. Those are contexts identified by Adam’s authors, based on the problems they studied; they are not guarantees that Adam will be faster or more accurate on every current model.
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The original paper reported tested settings of α = 0.001, β1 = 0.9, β2 = 0.999, and ε = 10−8 for its machine-learning problems. Treat these as historical paper settings, not as current defaults for a particular software framework or as an automatically suitable configuration for your task.
Which optimizer generalizes better?
Training progress and held-out performance are different outcomes. An optimizer can reduce training loss quickly without producing the best development or test result. In a 2017 comparative study, Wilson and colleagues reported that, with the same amount of hyperparameter tuning, SGD and SGD with momentum outperformed adaptive methods on the development/test sets across the models and tasks they evaluated. Their result is a reason to test SGD, not evidence that SGD always wins for every architecture, dataset, or modern optimizer variant. Read the study’s scope in The Marginal Value of Adaptive Gradient Methods in Machine Learning.
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- Use scikit-learn to track an example ML project end to end
- Explore several models, including support vector machines, decision trees, random forests, and ensemble methods
- Exploit unsupervised learning techniques such as dimensionality reduction, clustering, and anomaly detection
- Dive into neural net architectures, including convolutional nets, recurrent nets, generative adversarial networks, autoencoders, diffusion models, and transformers
- Use TensorFlow and Keras to build and train neural nets for computer vision, natural language processing, generative models, and deep reinforcement learning
How to make a fair comparison
- Choose the deployment-relevant metric. Decide whether you will select by held-out accuracy, loss, or another metric that reflects the model’s intended use. Establish a baseline before comparing optimizers.
- Train both candidates. Compare Adam with SGD; include SGD with momentum when appropriate for the task.
- Tune each method comparably. Give both optimizers a comparable search over learning rates and schedules, and a comparable training budget. Avoid comparing a tuned configuration with another method’s untuned defaults.
- Track training and validation separately. Follow both through training. Note whether validation performance plateaus even as training loss continues to improve.
- Select on reliable validation results. Keep data splits, architecture, compute budget, and evaluation metric fixed. If run-to-run variability could change which configuration appears best, repeat runs before deciding.
This comparison protocol is practical guidance for making a task-specific decision; it is not a checklist prescribed verbatim by either cited study.
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What the evidence does—and does not—establish
- The cited results do not establish a universal winner across all models, datasets, or optimizer variants.
- Adam’s ability to adapt update scales does not guarantee faster wall-clock training, better final accuracy, or less tuning on your problem.
- Although Adam is often presented as requiring little tuning, Wilson and colleagues found that all methods required tuning in the tasks they studied.
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