Dynamic Programming Explained in Less than 10 Minutes!

Today, we'll be going over an essential technique in every SWE's toolkit, known as dynamic programming. This technique is an optimization over plain naive recursion with the idea being we store results of subproblems to prevent redundant recursive calls. Fibonacci sequence numbers is a popular example to demonstrate dynamic programming, so we will break this problem down in Python. Some graphics were pulled from: https://www.geeksforgeeks.org/competitive-programming/dynamic-programming/ Big shoutout to @dontmakelies for her editing work! Check out my book Modern Data: From Ingestion to Production available on Amazon, Apple Books, and Barnes & Nobles: 🔗 Amazon 🚚 : https://www.amazon.com/dp/B0GH8J71SC 🔗 Barnes & Noble 📚: https://www.barnesandnoble.com/w/modern-data-kelvin-lin/1149201590? 🔗 Apple Books 🍎: https://books.apple.com/us/book/modern-data/id6757802062