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

```py
def func(num):
	if num <= 0:
 		return 0
	elif num % 2 == 0:
 		return num + func(num - 2)
 	else:
 		return num + func(num - 1) + 1
```

`func` function employs binary recursion

<pre><code class="language-py">def func(num):
	if num &lt;= 0:
 		return 0
	elif num % 2 == 0:
 		return num + func(num - 2)
 	else:
 		return num + func(num - 1) + 1
</code></pre>
<code>func</code> function employs binary recursion

```
def func(num):
	return func(num - 1) + num

```

What would `func(5)` return:

<pre><code>def func(num):
	return func(num - 1) + num

</code></pre>
What would <code>func(5)</code> return:

```
def func(str):
	if len(str) >= 1:
  		print(str[0])
    	func(str[1:])
    
```

What kind of recursion is this? 
(you can select multiple correct answers)

<pre><code>def func(str):
	if len(str) &gt;= 1:
 		print(str[0])
 	func(str[1:])
 
</code></pre>
What kind of recursion is this?
(you can select multiple correct answers)

Simple recursion is more related to which of the following words?

Simple recursion is more related to which of the following words?

All the algorithms that use loops can be converted into recursive algorithm

All the algorithms that use loops can be converted into recursive algorithm

Recursion is more similar to which of the following two approaches:

Recursion is more similar to which of the following two approaches:

After employing principles of dynamic programming, the Fibonacci numbers' algorithm's complexity drops from exponential to:

After employing principles of dynamic programming, the Fibonacci numbers’ algorithm’s complexity drops from exponential to:

A problem depends on different subproblems, but overlap of subproblems is small.

A problem depends on subproblems but optimal solution may not be built using the optimal solution to subproblems.

Optimal solution of the problem can be constructed using optimal solution of subproblems but overlap of subproblems is small.

A problem depends on different subproblems and overlap of subproblem is significant.

Identify the scenario where dynamic programming is more likely to work.

Identify the scenario where dynamic programming is more likely to work.

Finding all permutations of a string has which of the following properties:

(you can select multiple correct answers)

Finding all permutations of a string has which of the following properties:
(you can select multiple correct answers)

Quiz

Quiz

In this lesson, you will be quizzed on your knowledge of recursion and the basics of dynamic programming.


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