Dynamic Programming in Python: Optimizing Programs for Efficiency

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.

Intermediate

47 Lessons

Certificate of Completion

AI-POWERED

Explanations

AI-POWERED

Explanations

This course includes

57 Playgrounds

16 Challenges

This course includes

57 Playgrounds

16 Challenges

Course Overview

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...Show More

Course Content

Chapter 1: From Recursion to Dynamic Programming

Get familiar with transitioning from recursion to dynamic programming and optimizing algorithms efficiently.

Introduction What is Recursion?

Thinking Recursively The Fibonacci Numbers Challenge: Find All Permutations of a String Solution Review: Find All Permutations of a String Challenge: Place N Queens on an NxN Chessboard Solution Review: Place N Queens on an NxN Chessboard Is Plain Recursion Good Enough?What is Dynamic Programming?

Approaches of Dynamic Programming

Where to Use Dynamic Programming Quiz

Chapter 2: Top-Down Dynamic Programming with Memoization

Get started with efficient top-down dynamic programming using memoization techniques for optimization.

What is Memoization?The Fibonacci Numbers Algorithm with Memoization Challenge: The Staircase Problem Solution Review: The Staircase Problem Challenge 2: The Knapsack Problem Solution Review: The Knapsack Problem Quiz

Chapter 3: Bottom-Up Dynamic Programming with Tabulation

Work your way through bottom-up dynamic programming, tabulation, optimization, and practical problem-solving.

Limitations of Top-Down Dynamic Programming What is Tabulation?The Fibonacci Numbers Algorithm with Tabulation Optimizing the Fibonacci Numbers Algorithm Challenge: The Catalan Numbers Solution Review: The Catalan Numbers Challenge 2: Longest Common Substring Solution Review: Longest Common Substring Top-Down vs Bottom-Up Quiz

Chapter 4: Practice Problems

Apply your skills to solve diverse dynamic programming challenges efficiently and enhance problem-solving techniques.

Challenge: Number of Ways to Represent N Dollars Solution Review: Number of Ways to Represent N Dollars

Challenge: The Rod Cutting Problem

Solution Review: The Rod Cutting Problem Challenge: Weighted Scheduling Problem Solution Review: Weighted Scheduling Problem Challenge: The Matrix Chain Multiplication Solution Review: The Matrix Chain Multiplication Challenge: The Traveling Salesman Problem Solution Review: The Traveling Salesman Problem Challenge: Longest Common Subsequence Solution Review: Longest Common Subsequence Challenge: Longest Palindromic Subsequence Solution Review: Longest Palindromic Subsequence Challenge: The Edit Distance Problem Solution Review: The Edit Distance Problem Conclusion

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