Grokking Algorithms: An illustrated guide for programmers by Aditya Bhargava

By Aditya Bhargava

Summary

Grokking Algorithms is a completely illustrated, pleasant advisor that teaches you the way to use universal algorithms to the sensible difficulties you face on a daily basis as a programmer. you will commence with sorting and looking out and, as you building up your talents in considering algorithmically, you will take on extra advanced issues akin to facts compression and synthetic intelligence. every one rigorously offered instance comprises worthwhile diagrams and completely annotated code samples in Python.

Purchase of the print publication encompasses a unfastened publication in PDF, Kindle, and ePub codecs from Manning Publications.

About the Technology

An set of rules is not anything greater than a step by step approach for fixing an issue. The algorithms you will use normally as a programmer have already been came upon, proven, and confirmed. so that it will comprehend them yet refuse to slog via dense multipage proofs, this is often the booklet for you. This totally illustrated and fascinating advisor makes it effortless to benefit how you can use crucial algorithms successfully on your personal programs.

About the Book

Grokking Algorithms is a pleasant tackle this center desktop technological know-how subject. In it, you are going to tips on how to observe universal algorithms to the sensible programming difficulties you face each day. you will begin with projects like sorting and looking. As you building up your abilities, you will take on extra complicated difficulties like info compression and synthetic intelligence. each one rigorously awarded instance comprises worthwhile diagrams and completely annotated code samples in Python. through the top of this publication, you've got mastered generally appropriate algorithms in addition to how and whilst to exploit them.

What's Inside

  • Covers seek, kind, and graph algorithms
  • Over four hundred photos with particular walkthroughs
  • Performance trade-offs among algorithms
  • Python-based code samples

About the Reader

This easy-to-read, picture-heavy advent is acceptable for self-taught programmers, engineers, or somebody who desires to brush up on algorithms.

About the Author

Aditya Bhargava is a software program Engineer with a twin historical past in machine technology and high-quality Arts. He blogs on programming at adit.io.

Table of Contents

  1. Introduction to algorithms
  2. Selection sort
  3. Recursion
  4. Quicksort
  5. Hash tables
  6. Breadth-first search
  7. Dijkstra’s algorithm
  8. Greedy algorithms
  9. Dynamic programming
  10. K-nearest neighbors

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Additional info for Grokking Algorithms: An illustrated guide for programmers and other curious people

Sample text

Again, lists are better, because you just need to change what the previous element points to. With arrays, everything needs to be moved up when you delete an element. Unlike insertions, deletions will always work. Insertions can fail sometimes when there’s no space left in memory. But you can always delete an element. Here are the run times for common operations on arrays and linked lists. It’s worth mentioning that insertions and deletions are O(1) time only if you can instantly access the element to be deleted.

The call stack with recursion Recursive functions use the call stack too! Let’s look at this in action with the factorial function. , and it’s defined like this: 5! = 5 * 4 * 3 * 2 * 1. Similarly, factorial(3) is 3 * 2 * 1. Here’s a recursive function to calculate the factorial of a number: def fact(x): if x == 1: return 1 else: return x * fact(x-1) Now you call fact(3). Let’s step through this call line by line and see how the stack changes. Remember, the topmost box in the stack tells you what call to fact you’re currently on.

Big O notation 11 Algorithm running times grow at different rates Bob is writing a search algorithm for NASA. His algorithm will kick in when a rocket is about to land on the Moon, and it will help calculate where to land. This is an example of how the run time of two algorithms can grow at different rates. Bob is trying to decide between simple search and binary search. The algorithm needs to be both fast and correct. On one hand, binary search is faster. And Bob has only 10 seconds to figure out where to land—otherwise, the rocket will be off course.

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