Dynamic Programming (DP) = solve once, reuse many times.

## Fibonacci naive
```python
def fib(n):
    if n <= 1:
        return n
    return fib(n-1) + fib(n-2)
```

This repeats work.

## Memoized version
```python
def fib(n, memo={}):
    if n in memo:
        return memo[n]
    if n <= 1:
        return n
    memo[n] = fib(n-1) + fib(n-2)
    return memo[n]
```

## Graph
```mermaid
flowchart TD
  A[fib(5)] --> B[fib(4)]
  A --> C[fib(3)]
  B --> D[fib(3)]
  B --> E[fib(2)]
```

## Remember
- DP avoids recomputation
- memo = cache
- huge speedups


Understand dynamic programming as saving repeated work, with memoization and classic examples like Fibonacci.

Dynamic Programming Intro

Master fundamental data structures and their implementations.

Dynamic Programming Intro

Fibonacci naive

Memoized version

Graph

Remember

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