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Base.cs Podcast
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Base.cs Podcast
Base.cs Podcast
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For our final episode, we answer your burning questions including the Base.cs origin story, Saron and Vaidehi's favorite niche data structure, and what are some good resources to check out next. We also take a look back at some of our favorite
We've been talking a lot about the differences between compilers and interpreters, and how both of them work, and the ways that allowed one — the compiler — to lead to the creation of the other — the interpreter. Now we get into the Just In Tim
We have been talking a lot about compilers, and in this episode we discuss the differences between compilation versus interpretation. An interpreter is also a translator, just like a compiler, in that it takes a high level language (our source
In this episode, we take our parse tree, an illustrated, pictorial version of the grammatical structure of a sentence, and we take a metaphorical broom to sweep away repetitive bits, sliming it down, and leveling it up by creating an abstract s
In this episode, we get into what a compiler is and does. In short, a compiler is a program that reads our code (or any code, in any programming language), and translates it into another language. You'll want to listen in to find out just how i
In this episode, we get into parse trees, an illustrated, pictorial version of the grammatical structure of a sentence, which is important to understanding how computers understand coding syntax. Based on Vaidehi Joshi's blog post, "Grammatical
We continue our journey with the Traveling Salesman Problem (TSP), where this we imagine a salesperson has to travel to every single city in an area, visiting each city only once. Additionally, they need to end up in the same city where they st
We start our season off with something that often pops up in technical interviews: the Traveling Salesman Problem (TSP). In this problem, a salesperson has to travel to every single city in an area, visiting each city only once. Additionally, t
In this last episode of the season we continue our discussion of dynamic programming, and show just how efficient it can be by using the Fibonacci sequence! Based on Vaidehi Joshi's blog post, "Less Repetition, More Dynamic Programming".
In this episode we talk about different paradigms and approaches to algorithmic design: the Divide and Conquer Algorithm, the Greedy Algorithm, and the Dynamic Programming Algorithm, which remembers the subproblems that it has seen and solved b
We continue our talk about Dijkstra's algorithm, which can be used to determine the shortest path from one node in a graph to every other node within the same graph data structure, provided that the nodes are reachable from the starting node. B
In this episode, we talk about Dijkstra's algorithm, which can be used to determine the shortest path from one node in a graph to every other node within the same graph data structure, provided that the nodes are reachable from the starting nod
We end our section of the DFS algorithm with a discussion on DAGs (directed acyclic graphs), because most implementations of depth-first search will check to see if any cycles exist, and a large part of that is based on the DFS algorithm checki
Throughout our exploration of graphs, we’ve focused mostly on representing graphs, and how to search through them. We also learned about edges, the elements that connect the nodes in a graph. In this episode, we look at the different classifica
Last episode, we talked about traversing through a graph with the depth-first search (DFS) algorithm, which helps us determine one (of sometimes many) paths between two nodes in the graph by traversing down one single path until we can't go any
We ended last season by starting our discussion of searching, or traversing, through a graph with breadth-first search (BFS). The breadth-first search algorithm traverses broadly into a structure, by visiting neighboring sibling nodes before vi
In this episode, we start our discussion of searching, or traversing, through a graph with breadth-first search (BFS). The breadth-first search algorithm traverses broadly into a structure, by visiting neighboring sibling nodes before visiting
In this episode, we continue our discussion of representing graphs with adjacency lists -- a hybrid between an edge list and an adjacency matrix, which we learned about last episode! They are also the most popular and commonly-used representati
Graphs come from mathematics, and are nothing more than a way to formally represent a network, which is a collection of objects that are all interconnected (this is all stuff you should already know if you have been religiously listening to thi
In last episode, we talked about 2-3 trees, where the nodes of every tree contain data in the form of keys, as well as potential child nodes, and can contain more than one key. This takes us to b-trees, which is a generalized version of the 2-3
We continue our discussion of tree data structures with 2-3 trees, where the nodes of every tree contain data in the form of keys, as well as potential child nodes. Not only that, but it can contain MORE THAN ONE KEY. They are also the -key- to
In this episode, we are looking at a different type of self-balancing tree: red-black trees. By following four very important rules while we paint our tree red and black, we can make it not only self-balancing, but also make it run super effici
Last episode, we learned about AVL trees, a type of self-balancing binary search tree that follows a golden rule: no single leaf in the tree should have a significantly longer path from the root node than any other leaf on the tree. In this epi
When you're dealing with data structures like trees, the balance of its "leaves" (data/nodes) matters. The moment a tree becomes unbalanced, it loses its efficiency, much like a real life tree bending to the weight of one side, unable to effici
In this episode, we continue our talk on Radix Trees and introduce the Practical Algorithm To Retrieve Information Coded In Alphanumeric trees, also known as PATRICIA trees. Yeah, I think we'll just stick with calling them PATRICIA trees. Base
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