Gradient Descent from First Principles: Why Adam Outperforms SGD on Transformers

1. 1Apply gradient descent from first principles to understand optimizer behavior
2. 2Compare the performance of Adam and SGD on transformer models
3. 3Analyze the impact of adaptive learning rates on model convergence
4. 4Implement Adam optimizer in your model training pipeline to improve performance
5. 5Evaluate the trade-offs between Adam and SGD in terms of computational cost and accuracy

Machine learning engineers and researchers can benefit from understanding the differences between Adam and SGD optimizers to improve their model training workflows

💡 Adam's adaptive learning rate and momentum terms allow it to outperform SGD on complex models like transformers

🤖 Why Adam outperforms SGD on transformers? Learn from first principles! 🚀