Gain insights into dynamic programming, explore recursion basics, and delve into advanced techniques like Bottom-Up optimization. Discover ways to solve complex problems more efficiently with hands-on coding challenges.

Dynamic programming is something every developer should have in their toolkit. It allows you to optimize your algorithm with respect to time and space — a very important concept in real-world applications.

In this course, you’ll start by learning the basics of recursion and work your way to more advanced DP concepts like Bottom-Up optimization. Throughout this course, you will learn various types of DP techniques for solving even the most complex problems. Each section is complete with coding challenges of varying difficulty so you can practice a wide range of problems.

By the time you’ve completed this course, you will be able to utilize dynamic programming in your own projects.

Dynamic Programming in Python: Optimizing Programs for Efficiency

Dynamic programming can reduce the time complexity of all exponential algorithms.

Dynamic programming can reduce the time complexity of all exponential algorithms.

Dynamic programming reduces the time complexity of all the exponential algorithms that obey the properties of optimal substructure and overlapping subproblems to linear time.

Dynamic programming reduces the time complexity of all the exponential algorithms that obey the properties of optimal substructure and overlapping subproblems to linear time.

In the knapsack problem, time complexity is not linear even after memoization.

In the knapsack problem, time complexity is not linear even after memoization.

Top down dynamic programming consists of:

You can select multiple correct options.

Top down dynamic programming consists of:
You can select multiple correct options.

What could be potential limitation(s) of memoization?

You can select multiple correct options.

What could be potential limitation(s) of memoization?
You can select multiple correct options.

What is time complexity of this algorithm:

```
def fib(n, memo):
 if n <= 1:
 return 1
 elif n in memo:
 return memo[n]
 else:
 return fib(n-1, memo) + fib(n-2, memo)
```

What is time complexity of this algorithm:
<pre><code>def fib(n, memo):
 if n &lt;= 1:
 return 1
 elif n in memo:
 return memo[n]
 else:
 return fib(n-1, memo) + fib(n-2, memo)
</code></pre>

subproblems are evaluated before the main problem

main problem is evaluated before the subproblems

Call is made to the subproblems before the main problem.

Call is made to the main problem before the subproblem.

In this lesson, you will be tested on your knowledge of top-down dynamic programming and memoization.


Chapter 1: From Recursion to Dynamic Programming

Chapter 2: Top-Down Dynamic Programming with Memoization

Chapter 3: Bottom-Up Dynamic Programming with Tabulation

Chapter 4: Practice Problems

Quiz