Dynamic Programming in Python: Optimizing Programs for Efficiency

Description

In this course, you will learn :

• The fundamentals of recursion before progressing to more advanced DP concepts like Bottom-Up optimization.
• Learn a variety of DP techniques for dealing with even the most difficult problems.
• A variety of coding challenges of varying difficulty, allowing you to practise a wide range of problems.
• Able to use dynamic programming in your own projects.

Syllabus :

1. From Recursion to Dynamic Programming

• 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

 

2. Top-Down Dynamic Programming with Memoization

• What is Memoization?
• The Fibonacci Numbers Algorithm with Memoization

 

3. Bottom-Up Dynamic Programming with Tabulation

• Limitations of Top-Down Dynamic Programming
• What is Tabulation?
• The Fibonacci Numbers Algorithm with Tabulation
• Optimizing the Fibonacci Numbers Algorithm
• Top-Down vs Bottom-Up

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