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 moments from the show's history, and find a couple of fun themes. Based on Vaidehi Joshi's blog post, "Base.cs".

Base.cs Podcast

S9:E8 - "In the end, the code you take is equal to the code you make"

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 Time compiler, or a JIT, which is fusion or combination of the interpreter and the compiler, which are each two types of translators in their own right. A just-in-time compiler has many of the benefits of both of these two translation techniques, all rolled up into one. Based on Vaidehi Joshi's blog post, "A Most Perfect Union: Just-In-Time Compilers".

S9:E7 - "This way to translate is le-JIT"

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 text) and converts it into machine code. However, it does something slightly different: it actually runs and executes the code that it translates immediately (inline) as it translates. Based on Vaidehi Joshi's blog post, "A Deeper Inspection Into Compilation And Interpretation".

S9:E6 - "Two translators, both alike in dignity"

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 syntax tree (AST). Based on Vaidehi Joshi's blog post, "Leveling Up One’s Parsing Game With ASTs".

S9:E5 - "Paring down our parse trees with AST"

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 it does this! Based on Vaidehi Joshi's blog post, "Reading Code Right, With Some Help From The Lexer".

S9:E4 - "Confused about compilers?"

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, "Grammatically Rooting Oneself With Parse Trees".

S9:E3 - "Parsing out parse trees"

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 starts their journey from, and do this in the most efficient manner. However, in this episode, we are going to speed our salesperson up by using a bottom-up approach! Based on Vaidehi Joshi's blog post, "The Trials And Tribulations Of The Traveling Salesman".

S9:E2 - "Speeding up our traveling salesperson"

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, they need to end up in the same city where they starts their journey from. Find out how to make our salesperson do this in the most efficient way possible! Based on Vaidehi Joshi's blog post, "The Trials And Tribulations Of The Traveling Salesman".

S9:E1 - "Take a journey with the Traveling Salesman"

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".

S8:E8 - "Memoizing all the things in 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 before so as not to repeat doing the same thing over again. Based on Vaidehi Joshi's blog post, "Less Repetition, More Dynamic Programming".

S8:E7 - "Dynamic Programming is pretty dynamite "

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. Based on Vaidehi Joshi's blog post, "Finding The Shortest Path, With A Little Help From Dijkstra".

S8:E6 - "Getting deeper into Dijkastra"

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 node. It's super important, and you'll see why when you learn about the weighted graph! Based on Vaidehi Joshi's blog post, "Finding The Shortest Path, With A Little Help From Dijkstra".

S8:E5 - "Dijkstra's algorithm is a weighty topic"

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 checking to see whether or not a graph is a directed acyclic graph. DAGs are also somewhat infamous in computer science because they’re pretty much everywhere in sofware. For example, a directed acyclic graph is the backbone of applications that handle scheduling for systems of tasks or handling jobs — especially those that need to be processed in a particular order. So let's dig into DAGs! Based on Vaidehi Joshi's blog post, "Spinning Around In Cycles With Directed Acyclic Graphs".

S8:E4 - "DAG, Daniel! Back at it again..."

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 classifications of edges and how, in the context of a graph, edges can be more than just “directed” or “undirected”. Based on Vaidehi Joshi's blog post, "Spinning Around In Cycles With Directed Acyclic Graphs".

S8:E3 - "Living on the edge!"

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 further, checking one child node at a time. Now we talk about how you code BFS and what tools might you use. Based on Vaidehi Joshi's blog post, "Deep Dive Through A Graph: DFS Traversal".

S8:E2 - "Jump around the indexes with DFS!"

Beginner-friendly computer science lessons based on Vaidehi Joshi's base.cs blog series, produced by CodeNewbie.

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Base.cs Podcast

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