by Atul S. Khot, Raju Kumar Mishra | Released August 2017 | ISBN: 9781788390477

Get acquainted with complex data structures and algorithms with simple functional implementationsAbout This VideoBridge your transition from object-oriented programming to functional programmingEasy-to-understand explanations of practical topics will help you get started with functional data structuresIllustrative diagrams will explain the algorithms in detailExperience hands-on practice in Scala to get the most out of functional programming.In DetailAlgorithms and datastructures are fundamentals in computer programming. Functional data structures have the power to improve the codebase of an application and improve its efficiency. With the advent of functional programming and powerful functional languages such as Scala, Clojure, and Elixir becoming part of important enterprise applications, functional data structures have gained an important place in the developer toolkit.Immutability is a cornerstone of functional programming. Immutable and persistent data structures are thread-safe by definition and therefore are very appealing to write robust concurrent programs. But how do we express traditional algorithms in a functional setting? Won't we end up copying too much? Do we trade performance for versioned data structures? This course attempts to answer these questions by looking at functional implementations of traditional algorithms.The course begins by showing you the functioning of lists, the workhorse data type for most functional languages. We'll show you what structural sharing means and how it helps to make immutable data structures efficient and practical.While writing code, we use ADTs (abstract data types) such as Stacks, Queues, Trees, and Graphs. You'll see how these ADTs are implemented in a functional setting. We look at implementation techniques such as amortization and lazy evaluation to ensure efficiency. By the end of the course, you'll be able to write efficient functional data structures and algorithms for your applications. Show and hide more

1. Chapter 1 : Graph Algorithms
   • The Course overview 00:04:24
   • Reversing a List 00:03:29
   • Exploring More About Graph Algorithms 00:12:39
   • Cycle Detection 00:04:18
2. Chapter 2 : Random Access Lists
   • Incrementing a Binary Number 00:05:41
   • List of Tree Roots 00:16:24
3. Chapter 3 : Queues
   • FIFO Queues 00:04:15
   • Invariants 00:01:50
   • Priority Queue 00:07:44
   • Leftist Trees 00:02:07
   • Functional Heaps 00:08:06
4. Chapter 4 : Streams, Laziness, and Algorithms
   • Program Evaluation 00:03:46
   • Argument Evaluation 00:13:09
   • Memoization – Remembering Past Results 00:09:59
   • Streams 00:12:32
   • Some Algorithms on Stream 00:09:07
5. Chapter 5 : Being Lazy – Queues and Deques
   • Imperative Implementations and Amortization 00:04:39
   • Strict Versus Lazy 00:01:34
   • Streams 00:07:08
   • Amortized Deques 00:04:36
6. Chapter 6 : Red-Black Trees
   • Concept of Red-Black Trees 00:03:45
   • Working with Red-Black Trees 00:11:04
   • Verifying the Transformation 00:02:59
7. Chapter 7 : Binomial Heaps
   • Binomial Trees 00:03:08
   • A Binomial Heap 00:03:51
   • Binary Number Equivalence 00:07:52
8. Chapter 8 : Sorting
   • Stable and Unstable Sorting 00:02:34
   • Bubble Sort 00:07:40
   • Selection Sort 00:05:37
   • Insertion Sort 00:06:30
   • Merge Sort 00:08:20
   • Quick Sort 00:07:54
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