Automatic Differentiation vs Manual Differentiation
Developers should learn automatic differentiation when building or optimizing models that require gradients, such as in deep learning frameworks (e meets developers should learn manual differentiation when implementing custom algorithms in machine learning, physics simulations, or numerical optimization that require precise control over gradient calculations, such as in backpropagation for neural networks or solving differential equations. Here's our take.
Automatic Differentiation
Developers should learn automatic differentiation when building or optimizing models that require gradients, such as in deep learning frameworks (e
Automatic Differentiation
Nice PickDevelopers should learn automatic differentiation when building or optimizing models that require gradients, such as in deep learning frameworks (e
Pros
- +g
- +Related to: backpropagation, gradient-descent
Cons
- -Specific tradeoffs depend on your use case
Manual Differentiation
Developers should learn manual differentiation when implementing custom algorithms in machine learning, physics simulations, or numerical optimization that require precise control over gradient calculations, such as in backpropagation for neural networks or solving differential equations
Pros
- +It is essential for debugging automated differentiation tools, understanding the underlying mathematics of models, and in educational contexts to build foundational skills in calculus and computational methods
- +Related to: automatic-differentiation, numerical-differentiation
Cons
- -Specific tradeoffs depend on your use case
The Verdict
Use Automatic Differentiation if: You want g and can live with specific tradeoffs depend on your use case.
Use Manual Differentiation if: You prioritize it is essential for debugging automated differentiation tools, understanding the underlying mathematics of models, and in educational contexts to build foundational skills in calculus and computational methods over what Automatic Differentiation offers.
Developers should learn automatic differentiation when building or optimizing models that require gradients, such as in deep learning frameworks (e
Disagree with our pick? nice@nicepick.dev