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Blink 3 of 8 - The 5 AM Club
by Robin Sharma
Grokking Algorithms summary
Aditya Bhargava
Brief summary
Grokking Algorithms is an interactive and beginner-friendly book that explains complex algorithms in a simple way. It uses real-world examples and visualizations to help you understand and apply these important concepts.
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Table of Contents
Grokking Algorithms
Summary of key ideas
Understanding the Fundamentals of Algorithms
In Grokking Algorithms by Aditya Bhargava, we begin with an introduction to algorithms. The author explains that an algorithm is a set of instructions for solving a problem or accomplishing a task. He uses the concept of a recipe to explain algorithms, highlighting that just as a recipe tells you how to cook a dish, an algorithm tells you how to solve a problem. The book then delves into basic algorithms like selection sort, which is a simple sorting algorithm.
Next, Bhargava introduces the concept of recursion, where a function calls itself in order to solve smaller instances of the same problem. He uses the example of a factorial function to illustrate this concept. Then, he moves on to quicksort, a more efficient sorting algorithm that uses the divide-and-conquer strategy, and hash tables, a data structure that provides fast insertion, deletion, and lookup of key-value pairs.
Exploring Graph Algorithms and Their Applications
As we move forward in Grokking Algorithms, Bhargava introduces us to graph algorithms, starting with breadth-first search (BFS). BFS is used to find the shortest path in an unweighted graph. The author provides a detailed explanation of how BFS works, using the example of a social network to demonstrate its application.
Following BFS, we learn about Dijkstra's algorithm, a more complex graph algorithm used to find the shortest path in a weighted graph. Bhargava uses the example of a road map to explain how Dijkstra's algorithm works and its real-world applications, such as GPS navigation systems.
Understanding Optimization with Greedy Algorithms and Dynamic Programming
In the latter part of Grokking Algorithms, we explore optimization strategies using greedy algorithms and dynamic programming. Greedy algorithms make the best local choice at each step with the hope of finding the global optimum, while dynamic programming breaks down a problem into smaller subproblems and solves each subproblem just once.
Bhargava illustrates the concepts of greedy algorithms and dynamic programming with real-world examples, such as the fractional knapsack problem and the knapsack problem. He shows us how these algorithms can be used to solve optimization problems, such as maximizing value with limited resources.
Applying Machine Learning Algorithms
Finally, Grokking Algorithms takes us into the world of machine learning with an introduction to the k-nearest neighbors (k-NN) algorithm. K-NN is a simple machine learning algorithm used for classification and regression tasks. Bhargava explains the working of k-NN and its applications in areas like recommendation systems and pattern recognition.
In conclusion, Grokking Algorithms by Aditya Bhargava provides an accessible and engaging introduction to fundamental algorithms and data structures. The book's clear explanations, illustrated examples, and real-world applications make it an ideal resource for beginners and self-taught programmers looking to understand and implement algorithms in their projects.
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What is Grokking Algorithms about?
Grokking Algorithms is a friendly and practical guide that takes you on a journey through fundamental computer algorithms. Written by Aditya Bhargava, the book uses real-world examples and simple language to help you understand complex concepts. Whether you're new to programming or looking to refresh your knowledge, this book will equip you with the essential skills to tackle algorithmic problems.
Grokking Algorithms Review
Grokking Algorithms (2016) is a practical guide to understanding important computer science algorithms. Here's why this book is worth your time:
- Explains complex algorithms with everyday examples that are easy to grasp, even for beginners.
- Uses visualizations and diagrams effectively to aid comprehension and make the topics more engaging.
- Offers a hands-on approach with exercises and code samples, ensuring active learning and practical application of the concepts.
Who should read Grokking Algorithms?
Individuals who want to understand and apply common algorithms to solve practical problems
Programmers and software developers looking to improve their problem-solving and coding skills
Students or professionals in computer science or related fields who want a beginner-friendly introduction to algorithms
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Book summaries like Grokking Algorithms
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Grokking Algorithms FAQs
What is the main message of Grokking Algorithms?
Understanding algorithms through practical examples and easy-to-understand explanations.
How long does it take to read Grokking Algorithms?
Reading time varies, but usually takes a few hours. The Blinkist summary can be read quickly.
Is Grokking Algorithms a good book? Is it worth reading?
Grokking Algorithms is a must-read for simplifying complex algorithms effectively. It's worth your time.
Who is the author of Grokking Algorithms?
Aditya Bhargava.
What to read after Grokking Algorithms?
If you're wondering what to read next after Grokking Algorithms, here are some recommendations we suggest:
- Big Data by Viktor Mayer-Schönberger and Kenneth Cukier
- Physics of the Future by Michio Kaku
- On Intelligence by Jeff Hawkins and Sandra Blakeslee
- Brave New War by John Robb
- Abundance# by Peter H. Diamandis and Steven Kotler
- The Signal and the Noise by Nate Silver
- You Are Not a Gadget by Jaron Lanier
- The Future of the Mind by Michio Kaku
- The Second Machine Age by Erik Brynjolfsson and Andrew McAfee
- Out of Control by Kevin Kelly