---
title: 'Dynamic Programming in Under 60 Seconds'
source: 'https://youtube.com/watch?v=conYkW6FAmk'
video_id: 'conYkW6FAmk'
date: 2026-08-04
duration_sec: 60
---

# Dynamic Programming in Under 60 Seconds

> Source: [Dynamic Programming in Under 60 Seconds](https://youtube.com/watch?v=conYkW6FAmk)

## Summary

This video provides a concise, under-60-second introduction to dynamic programming using a classic grid pathfinding example. It demonstrates how to break a complex problem into smaller subproblems and solve it efficiently by building a solution iteratively.

### Key Points

- **Problem Setup** [00:01] — A grid is presented where the goal is to reach the bottom-right cell from the top-left, moving only right or down. Brute force is exponentially slow due to too many paths.
- **Key Insight** [00:14] — To reach any cell, you can only come from the top or from the left. Thus, the number of ways to reach a cell equals the sum of ways to reach the cell above and the cell to the left.
- **Base Cases** [00:28] — For the first row and first column, there is only one way to reach each cell (moving only right or only down).
- **Building the Solution** [00:40] — Start filling the array from index (1,1) where the value is 1+1=2, and continue building the array until reaching the destination. This is dynamic programming.
- **Conclusion** [00:53] — Dynamic programming involves identifying smaller pieces and breaking the problem into those pieces. Recognizing this pattern is key to solving DP problems.

### Conclusion

Dynamic programming is a powerful technique that turns exponential brute-force problems into polynomial-time solutions by breaking them into overlapping subproblems and building up the answer iteratively.

## Transcript

Let me prove it in under 60 seconds with an example. You have a grid. You start want to reach here on the bottom. But you can only move right or down. So, how destination? Most people will try brute force, but there's too many paths and
it's exponentially slow. A better idea? Watch this. To reach any cell, you only from the top or come from the left. That's it. So, the number of ways to ways required to reach the top cell plus the number of ways required to reach the
start filling the grid. For the first row, there's only one way. And similarly, for the first column, there's also only one way. Now, we just have to build the array starting from index 1 1, which has 1 + 1 two ways to reach it. We
can just keep on building the array and in the end we reach our destination with dynamic programming. Identify smaller pieces and break it into those pieces. don't see this pattern, dynamic programming problems will always feel
looking the same. Want to learn more dynamic programming problems? Like this dynamic programming problems? Like this video and follow us for more.