Join the discussion on Reddit I started the Let's Learn Algorithms series originally by writing tutorials that would: Teach how an algorithm works Demonstrate how to implement that algorithm in code Provide some practice problems so you can get real practice implementing the algorithm in different scenarios (2) and (3) are in my opinion incredibly important pieces to the puzzle that are often missing in many other courses, so I really try to give them just as much focus as the initial lesson that just teaches how the algorithm works.

See https://play.golang.org/p/u4E6mrios0 for the completed code from this video. In this video we learn how to represent a binary tree in Go code. Once we have the basic structure in place, we then go a step further and define an input file format that we can use to read in arbitrary binary trees moving forward to test our algorithms with. This post is part of the Let's Learn Algorithms series where we learn how algorithms work, see how to implement them, and then spend some time working on practice problems to reinforce how the implementation details work, as well as to help you learn to recognize problems that could be solved using any particular algorithm.

In this video we cover how the depth first search algorithm works. We do so by starting with a binary tree and walking through how the algorithm would iterate over the tree searching for a specific node. We intentionally start with a tree because this removes a lot of complicating factors that might be present in other graphs, such as running into a cycle, but everything you learn here will be applicable to when we start working with cyclical graphs to also run our DFS.

In this video we discuss a specific type of graph - the binary tree. In the video we discuss some of the properties of a tree that make is a special type of graph, and then we discuss a few common properties of trees that aren’t always present, but are often there in order to make trees a more optimal data structure to work with. This is all used to set the stage for upcoming videos where we start to implement some algorithms first using trees, and then later on more general graphs.

In this video we cover all of the basics about graphs. We start be defining the different parts of a graph, and then jump into a real world example where we translate a map with cities and highways into a graph. Finally, we discuss some of the common attributes you might find on a graph, such as cost, capacity, directional edges, and even cycles, and relate these all to our graph derived from a map of cities and highways.

I posted this recently on Reddit, but I felt it was important enough to post again here. One of the most common themes I see with beginners or people learning a new language is the question “What should I build?” The problem isn’t the question itself, but that so many people want to build something that is both: Useful to everyone (eg may be a popular open source project), and Simple enough that they could build it as their 1st, 2nd, or even 5th project.