web site hit counter Algorithms Illuminated (Part 1): The Basics - Ebooks PDF Online
Hot Best Seller

Algorithms Illuminated (Part 1): The Basics

Availability: Ready to download

Algorithms are the heart and soul of computer science. Their applications range from network routing and computational genomics to public-key cryptography and database system implementation. Studying algorithms can make you a better programmer, a clearer thinker, and a master of technical interviews. Algorithms Illuminated is an accessible introduction to the subject---a t Algorithms are the heart and soul of computer science. Their applications range from network routing and computational genomics to public-key cryptography and database system implementation. Studying algorithms can make you a better programmer, a clearer thinker, and a master of technical interviews. Algorithms Illuminated is an accessible introduction to the subject---a transcript of what an expert algorithms tutor would say over a series of one-on-one lessons. The exposition is rigorous but emphasizes the big picture and conceptual understanding over low-level implementation and mathematical details. Part 1 of the book series covers asymptotic analysis and big-O notation, divide-and-conquer algorithms and the master method, randomized algorithms, and several famous algorithms for sorting and selection.


Compare

Algorithms are the heart and soul of computer science. Their applications range from network routing and computational genomics to public-key cryptography and database system implementation. Studying algorithms can make you a better programmer, a clearer thinker, and a master of technical interviews. Algorithms Illuminated is an accessible introduction to the subject---a t Algorithms are the heart and soul of computer science. Their applications range from network routing and computational genomics to public-key cryptography and database system implementation. Studying algorithms can make you a better programmer, a clearer thinker, and a master of technical interviews. Algorithms Illuminated is an accessible introduction to the subject---a transcript of what an expert algorithms tutor would say over a series of one-on-one lessons. The exposition is rigorous but emphasizes the big picture and conceptual understanding over low-level implementation and mathematical details. Part 1 of the book series covers asymptotic analysis and big-O notation, divide-and-conquer algorithms and the master method, randomized algorithms, and several famous algorithms for sorting and selection.

30 review for Algorithms Illuminated (Part 1): The Basics

  1. 4 out of 5

    Michael Driscoll

    This book is almost verbatim the transcripts of the lectures from the Coursera course "Divide and Conquer, Sorting and Searching, and Randomized Algorithms" of the Algorithms Specialization, sections I-VIII. That's not necessarily a bad thing, but just something to keep in mind. On its own I'd say the book is a good textbook, but its real purpose is to be supplementary material to the Coursera lectures. The biggest caveat is that this book leaves out section IX Graphs and the Contraction Algorith This book is almost verbatim the transcripts of the lectures from the Coursera course "Divide and Conquer, Sorting and Searching, and Randomized Algorithms" of the Algorithms Specialization, sections I-VIII. That's not necessarily a bad thing, but just something to keep in mind. On its own I'd say the book is a good textbook, but its real purpose is to be supplementary material to the Coursera lectures. The biggest caveat is that this book leaves out section IX Graphs and the Contraction Algorithm, for those details you'll need Part 2. To be fair to the author, it fits better in with the second book as it's all about graphs, but leaves out details that are helpful with the final programming assignment of the first course. In Summary: If you want to learn Algorithms on your own, this isn't for you. If you are taking the Coursera Algorithms Specialization and find it easier to read than listen, this is a good book to get. Also, once Roughgarden published his book he removed the suggested readings from the other Algo text books, which... I find a bit annoying. The weeks below don't line up to what you see on Coursera as originally the courses were two six-week courses as opposed to four four-week courses. Here are the other textbooks readings: CLRS - Cormen, Leiserson, Rivest, and Stein, Introdution to Algorithms (3rd edition) DPV - Dasgupta, Papadimitriou, and Vazirani, Algorithms KT - Kleinberg and Tardos, Algorithm Design SW - Sedgewick and Wayne, Algorithms (4th edition) Week 1 (Merge Sort, Asymptotic Notation, Guiding Principles of Algorithm Analysis, Divide & Conquer Algorithms) CLRS: Chapter 2, 3, and 4 (through Section 4.2), and Sections 28.1 and 33.4 DPV: Sections 0.3, 2.1, 2.3, 2.5 KT: Sections 2.1, 2.2, 2.4, 5.1, and 5.3-5.5 SW: Sections 1.4 and 2.2 Week 2 (Master Method, QuickSort) CLRS: Chapter 4 (Sections 4-6) and Chapter 7 DPV: Section 2.2 KT: Sections 5.2 and 13.5 SW: Section 2.3 Week 3 (Final Thoughts on Sorting & Searching, Introduction to Graph Algorithms : Graph Representations & Minimum Cuts in Graphs) CLRS: Chapter 9, 22 (Only 22.1) DPV: Chapter 3 (only 3.1) KT: Chapter 13, Sections 13.2,13.5 SW: Chapter 4, Section 4.1 Week 4 (Graph Search: Breadth-First Search, Depth-First Search, Applications: Topological Sort, Connected Components) CLRS: Chapter 22 DPV: Chapter 3 KT: Chapter 3, Section 3.5, 3.6 SW: Chapter 4, Section 4.1,4.2 Week 5 (Dijkstra's Shortest-Path Algorithm, Data structures and how to use them, Heaps, Binary Search Trees, Balanced BSTs) CLRS: Chapter 6,11,12,13 24 (Sections 3,4) DPV: Section 1.5 KT: Section 4.4 SW: Section 3.3, 3.4, 4.4 Week 6 (Hash Tables: Applications and Implementation, Bloom Filters) CLRS: Chapter 11 KT: Chapter 13 (Section 13.6) SW: Section 3.5

  2. 5 out of 5

    Arun

    Like a big brother of "Algorithms Unlocked". Text is an almost direct transcript of video lectures which positively contributed to the flow of chapters. Felt like a perfect balance of rigor and verbal enunciation. Didn't think it could topple "Algorithms Unlocked" in narrative flow but boy it did! Like a big brother of "Algorithms Unlocked". Text is an almost direct transcript of video lectures which positively contributed to the flow of chapters. Felt like a perfect balance of rigor and verbal enunciation. Didn't think it could topple "Algorithms Unlocked" in narrative flow but boy it did!

  3. 4 out of 5

    Argum

    I won a free copy of this book from Goodreads First Reads. This book isn't bad, but it is not accessible at least at the level the blurb lead me to expect. It is well structured building knowledge, but it has a heavy dose of theory and math. I regularly code in several languages and have taken actual classes and built some of these algorithms. I still struggled to follow some of this text. Plus book is formatted in Latex which is annoying to read. I won a free copy of this book from Goodreads First Reads. This book isn't bad, but it is not accessible at least at the level the blurb lead me to expect. It is well structured building knowledge, but it has a heavy dose of theory and math. I regularly code in several languages and have taken actual classes and built some of these algorithms. I still struggled to follow some of this text. Plus book is formatted in Latex which is annoying to read.

  4. 5 out of 5

    Risto Hinno

    One of those books that neatly combine mathematics and computers. Good beginning to understand basics of analyzing algorithms. And of course some magic how some pretty simple solutions achieve blazingly fast running time. I like that book has supplementary materials which help to dig more deeply into subject. Also it has practical challenges (like coding) which is essential understanding what is really going on in certain algorithm.

  5. 4 out of 5

    Heather Fryling

    The Algorithms Illuminated series is a companion to Tim Roughgarden's series of algorithms classes on Coursera. The books contain expanded transcripts of the lectures. I found book one extremely helpful as a reference both for the class and other classes, as it contains good pseudocode and explanations of many canonical divide and conquer algorithms. The appendices on proofs by induction and discrete probability are extremely helpful for a quick refresher. The only issue is that there are no ans The Algorithms Illuminated series is a companion to Tim Roughgarden's series of algorithms classes on Coursera. The books contain expanded transcripts of the lectures. I found book one extremely helpful as a reference both for the class and other classes, as it contains good pseudocode and explanations of many canonical divide and conquer algorithms. The appendices on proofs by induction and discrete probability are extremely helpful for a quick refresher. The only issue is that there are no answers provided for the practice problems.

  6. 5 out of 5

    Eric Hulburd

    Simple and clear. Good for jogging the basics of runtime analysis or as an introduction.

  7. 5 out of 5

    Anthony O'Connor

    Solid introduction BigO, divide and conquer. The subtle beauty of quicksort and mergesort. Modern day ABCs. Good examples. Thorough analysis without being too long winded.

  8. 4 out of 5

    Cormac D

    A great introductory textbook to the subject of computer science, i would reccomend.

  9. 5 out of 5

    Fatima

    Tim Roughgarden's Stanford Lectures on Coursera are amazing. This book covers the first part of the his lectures on Coursera. It's very important to understand the intuition for why an algorithm is correct. It is equally important to know the running time analysis of an algorithm. CLRS is the computer science Bible for that but it's dry AF (don't get me wrong, I adore CLRS). Tim's approach is definitely more intuitive, friendlier and makes the process more enjoyable :) Tim Roughgarden's Stanford Lectures on Coursera are amazing. This book covers the first part of the his lectures on Coursera. It's very important to understand the intuition for why an algorithm is correct. It is equally important to know the running time analysis of an algorithm. CLRS is the computer science Bible for that but it's dry AF (don't get me wrong, I adore CLRS). Tim's approach is definitely more intuitive, friendlier and makes the process more enjoyable :)

  10. 4 out of 5

    Yaroslav A

  11. 4 out of 5

    Ram Arunachalam

  12. 5 out of 5

    sprite

  13. 4 out of 5

    Rakesh

  14. 5 out of 5

    Gary Lai

  15. 4 out of 5

    Christopher Ngo

  16. 4 out of 5

    Matthew Horvat

  17. 4 out of 5

    Sherif Ashraf

  18. 5 out of 5

    Snakile

  19. 5 out of 5

    Greg

  20. 4 out of 5

    Srikar Mylavarapu

  21. 4 out of 5

    Vladislav Gangan

  22. 4 out of 5

    Martin Brant

  23. 4 out of 5

    NSLog0

  24. 4 out of 5

    Jovany Agathe

  25. 4 out of 5

    Dixon Liang

  26. 4 out of 5

    Dale Alleshouse

  27. 4 out of 5

    James Stevenson

  28. 4 out of 5

    Caleb Fowler

  29. 5 out of 5

    Marcin Krasowski

  30. 4 out of 5

    rozmov

Add a review

Your email address will not be published. Required fields are marked *

Loading...
We use cookies to give you the best online experience. By using our website you agree to our use of cookies in accordance with our cookie policy.