Optimizers Explained: SGD vs Momentum vs Adam, Visualized
📰 Dev.to · Devanshu Biswas
Learn how SGD, Momentum, and Adam optimizers affect training speed and stability in machine learning, with interactive visualizations
Action Steps
- Visualize the optimizers' performance using the provided interactive tool at https://dev48v.infy.uk/dl/day7-optimizers.html
- Compare the training speed and stability of SGD, Momentum, and Adam optimizers
- Apply the chosen optimizer to your own machine learning model to see the impact on training
- Use SGD for simple, well-conditioned problems, Momentum for problems with steep slopes, and Adam for adaptive learning rate adjustment
- Experiment with different optimizers and hyperparameters to find the best combination for your specific problem
Who Needs to Know This
Machine learning engineers and data scientists can benefit from understanding the differences between these optimizers to improve model training efficiency and accuracy
Key Insight
💡 The choice of optimizer significantly affects training speed and stability, and understanding their differences is crucial for effective machine learning model development
Share This
🚀 Boost your ML model's training speed and stability by choosing the right optimizer! 🤔
Key Takeaways
Learn how SGD, Momentum, and Adam optimizers affect training speed and stability in machine learning, with interactive visualizations
Full Article
Title: Optimizers Explained: SGD vs Momentum vs Adam, Visualized
URL Source: https://dev.to/dev48v/optimizers-explained-sgd-vs-momentum-vs-adam-visualized-16pg
Published Time: 2026-06-20T07:01:32Z
Markdown Content:
# Optimizers Explained: SGD vs Momentum vs Adam, Visualized - DEV Community
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[Devanshu Biswas](https://dev.to/dev48v)
Posted on Jun 20
# Optimizers Explained: SGD vs Momentum vs Adam, Visualized
[#machinelearning](https://dev.to/t/machinelearning)[#ai](https://dev.to/t/ai)[#deeplearning](https://dev.to/t/deeplearning)[#beginners](https://dev.to/t/beginners)
Backprop tells you which way is downhill. The OPTIMIZER decides how to actually step — and that choice hugely changes training speed and stability. Here's SGD vs Momentum vs Adam, racing down the same valley.
⚙️ **Watch them race:**[https://dev48v.infy.uk/dl/day7-optimizers.html](https://dev48v.infy.uk/dl/day7-optimizers.html)
Real loss surfaces are "ill-conditioned" — steep one way, nearly flat another (a long narrow valley). Each optimizer copes differently:
## [](https://dev.to/dev48v/optimizers-explained-sgd-vs-momentum-vs-adam-visualized-16pg#sgd-plain-step) SGD — plain step
URL Source: https://dev.to/dev48v/optimizers-explained-sgd-vs-momentum-vs-adam-visualized-16pg
Published Time: 2026-06-20T07:01:32Z
Markdown Content:
# Optimizers Explained: SGD vs Momentum vs Adam, Visualized - DEV Community
[Skip to content](https://dev.to/dev48v/optimizers-explained-sgd-vs-momentum-vs-adam-visualized-16pg#main-content)
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[Devanshu Biswas](https://dev.to/dev48v)
Posted on Jun 20
# Optimizers Explained: SGD vs Momentum vs Adam, Visualized
[#machinelearning](https://dev.to/t/machinelearning)[#ai](https://dev.to/t/ai)[#deeplearning](https://dev.to/t/deeplearning)[#beginners](https://dev.to/t/beginners)
Backprop tells you which way is downhill. The OPTIMIZER decides how to actually step — and that choice hugely changes training speed and stability. Here's SGD vs Momentum vs Adam, racing down the same valley.
⚙️ **Watch them race:**[https://dev48v.infy.uk/dl/day7-optimizers.html](https://dev48v.infy.uk/dl/day7-optimizers.html)
Real loss surfaces are "ill-conditioned" — steep one way, nearly flat another (a long narrow valley). Each optimizer copes differently:
## [](https://dev.to/dev48v/optimizers-explained-sgd-vs-momentum-vs-adam-visualized-16pg#sgd-plain-step) SGD — plain step
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