Analytical Differentiation vs Automatic Differentiation

Developers should learn analytical differentiation when working on problems requiring exact derivatives, such as in gradient-based optimization algorithms (e meets developers should learn automatic differentiation when building or optimizing models that require gradients, such as in deep learning frameworks (e. Here's our take.

Analytical Differentiation

Nice Pick

Developers should learn analytical differentiation when working on problems requiring exact derivatives, such as in gradient-based optimization algorithms (e

Pros

• g
• Related to: numerical-differentiation, automatic-differentiation

Cons

• Specific tradeoffs depend on your use case

Automatic Differentiation

Developers 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

The Verdict

Use Analytical Differentiation if: You want g and can live with specific tradeoffs depend on your use case.

Use Automatic Differentiation if: You prioritize g over what Analytical Differentiation offers.

The Bottom Line

Analytical Differentiation wins

Developers should learn analytical differentiation when working on problems requiring exact derivatives, such as in gradient-based optimization algorithms (e