Extended Courses Discount
My Go courses are discounted for the next few weeks to help out anyone who may need or want access to them. I'm also going to try to help out anyone who can't afford a course, and I will be writing posts about working from home over the next week in an attempt to help anyone new to WFH. Read more here.
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.
Jon Calhoun is a full stack web developer who also teaches about Go, web development, algorithms, and anything programming related. He also consults for other companies who have development needs. (If you need some development work done, get in touch!)
Jon is a co-founder of EasyPost, a shipping API that many fortune 500 companies use to power their shipping infrastructure, and prior to founding EasyPost he worked at google as a software engineer.
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