Grokking Algorithms Video Edition

Grokking Algorithms Video Edition

English | MP4 | AVC 1280×720 | AAC 44KHz 2ch | 3h 49m | 1.12 GB

An illustrated guide for programmers and other curious people

Grokking Algorithms is a fully illustrated, friendly guide that teaches you how to apply common algorithms to the practical problems you face every day as a programmer. You’ll start with sorting and searching and, as you build up your skills in thinking algorithmically, you’ll tackle more complex concerns such as data compression and artificial intelligence. Each carefully presented example includes helpful diagrams and fully annotated code samples in Python.

Grokking Algorithms is a friendly take on this core computer science topic. In it, you’ll learn how to apply common algorithms to the practical programming problems you face every day. You’ll start with tasks like sorting and searching. As you build up your skills, you’ll tackle more complex problems like data compression and artificial intelligence. Each carefully presented example includes helpful diagrams and fully annotated code samples in Python. By the end of this video editions course, you will have mastered widely applicable algorithms as well as how and when to use them.

An algorithm is nothing more than a step-by-step procedure for solving a problem. The algorithms you’ll use most often as a programmer have already been discovered, tested, and proven. If you want to understand them but refuse to slog through dense multipage proofs, you are at the right place. This fully illustrated and engaging guide makes it easy to learn how to use the most important algorithms effectively in your own programs.

Inside:

  • Covers search, sort, and graph algorithms
  • Over 400 pictures with detailed walkthroughs
  • Performance trade-offs between algorithms
  • Python-based code samples

This easy-to-read, picture-heavy introduction is suitable for self-taught programmers, engineers, or anyone who wants to brush up on algorithms.

Table of Contents

01 Introduction to Algorithms
02 A Better Way to Search
03 Algorithm running times grow at different rates
04 Some common Big O run times
05 Selection Sort
06 Terminology
07 Recursion
08 The Stack
09 Quicksort – 1st part
10 Quicksort – 2nd part
11 Big O notation revisited
12 Hash Tables
13 Use Cases
14 Collisions
15 Performance
16 Breadth-first Search
17 Finding the shortest path
18 Implementing the graph
19 Dijkstra’s algorithm
20 Trading for a piano
21 Negative-weight edges
22 Greedy Algorithms
23 Approximation algorithms
24 NP-complete problems
25 Dynamic Programming – 1st part
26 Dynamic programming – 2nd part
27 Can you steal fractions of an item
28 Longest common substring
29 K-nearest neighbors
30 Regression
31 OCR
32 Where to go next
33 MapReduce
34 The SHA algorithms
35 Diffie-Hellman key exchange