by Atul S. Khot, Raju Kumar Mishra | Released June 2017 | ISBN: 9781788393256

Learn functional data structures and algorithms for your applications and bring their benefits to your work nowAbout This VideoMoving from Object-Oriented Programming to functional programming? This book will help you get started with functional programming.Easy-to-understand explanations of practical topics will help you get started with functional data structures.Illustrative diagrams explain algorithms in detail.Get hands-on with Scala to get the most out of functional programming.In DetailFunctional data structures have the power to improve the code base of an application and improve efficiency. With the advent of functional programming, and with 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 hence very appealing for writing robust concurrent programs. 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.It begins with a refresher and consolidates what functional programming is all about. Next, you'll get to know about Lists, the work horse data type for most functional languages. We show what structural sharing means and how it helps to make immutable data structures efficient and practical. Moving on, you will learn about binary trees, where we will be building complete trees, greedy algorithms, and back tracking Show and hide more

1. Chapter 1 : Why Functional Programming?
   • The Course Overview 00:04:21
   • The Imperative Way and a Higher Level of Abstraction 00:06:25
   • Functional Programming and Boilerplate 00:04:30
   • Higher Order Functions 00:03:14
   • Recursion Aids Immutability 00:02:05
   • Copy-On-Write, Laziness, and Deferred Execution 00:03:54
   • Composing Functions 00:04:18
2. Chapter 2 : Building Blocks
   • The Big O Notation 00:03:15
   • Space/Time Trade-Off 00:06:08
   • Referential Transparency 00:03:06
   • Vectors Versus Lists 00:05:55
   • Complexities and Collections 00:09:20
3. Chapter 3 : Lists
   • First Steps 00:06:44
   • List Head and Tail 00:02:25
   • Drop Elements 00:02:28
   • Concatenating Lists 00:02:19
   • Persistent Data Structures and Tail Call Optimization 00:04:16
   • List Append and Prepend 00:03:32
   • Getting the Value at Index 00:02:05
   • Modifying a List Value 00:02:33
4. Chapter 4 : Binary Trees
   • Node Definitions 00:01:54
   • Building the Tree 00:04:14
   • Comparing Trees 00:07:52
   • The Accumulator Idiom 00:02:33
   • Binary Search Trees 00:07:24
5. Chapter 5 : More List Algorithms
   • Binary Numbers 00:07:26
   • Greedy Algorithms and Backtracking 00:06:04
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