English | MP4 | AVC 1920×1080 | AAC 48KHz 2ch | 3.5 Hours | 732 MB
Get acquainted with complex data structures and algorithms with simple functional implementations
Algorithms 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.
What You Will Learn
- Understand how to think in the functional paradigm
- Build cost-efficient applications
- Explore important algorithms for ordering dependencies
- See common data structures and the associated algorithms, and the context they are commonly used in
- See how ADTs are implemented in a functional setting
- Explore the basic theme of immutability and persistent data structures
- Find out how the internal algorithms are redesigned to exploit structural sharing, so that the persistent data structures perform well, avoiding needless copying
- Understand functional features such as lazy evaluation and recursion used to implement efficient algorithms
- Get to know Scala’s best practices and idioms
Table of Contents
01 The Course Overview
02 Reversing a List
03 Exploring More About Graph Algorithms
04 Cycle Detection
05 Incrementing a Binary Number
06 List of Tree Roots
07 FIFO Queues
08 Invariants
09 Priority Queue
10 Leftist Trees
11 Functional Heaps
12 Program Evaluation
13 Argument Evaluation
14 Memoization – Remembering Past Results
15 Streams
16 Some Algorithms on Stream
17 Imperative Implementations and Amortization
18 Strict Versus Lazy
19 Streams
20 Amortized Deques
21 Concept of Red-Black Trees
22 Working with Red-Black Trees
23 Verifying the Transformation
24 Binomial Trees
25 A Binomial Heap
26 Binary Number Equivalence
27 Stable and Unstable Sorting
28 Bubble Sort
29 Selection Sort
30 Insertion Sort
31 Merge Sort
32 Quick Sort
Resolve the captcha to access the links!