Learn ML with Real Code Examples

Immutable data may create overhead for large structures

Tail recursion optimization improves performance

Compiled ML code is efficient for symbolic computation

Use arrays for performance-critical numeric tasks

Garbage collection manages memory automatically

ML is type-safe, reducing common memory errors

No direct access to raw pointers in standard ML

Side effects are limited, improving reliability

Modules can encapsulate sensitive logic

Safe for formal verification tasks

Track program correctness via type system

Monitor performance of recursive functions

Log module interactions

Profile memory usage in large symbolic computations

Check REPL outputs during development

Document types and module interfaces

Use pattern matching exhaustively

Test functions in REPL incrementally

Structure code into reusable modules

Ensure type safety and immutability