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Grokking Algorithms
Audible Audiobook
– Unabridged
Grokking Algorithms is a 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. Learning about algorithms doesn't have to be boring!
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, this is the book for you. This engaging guide makes it easy to learn how to use the most important algorithms effectively in your own programs.
Contents include:
- Introduction to algorithms
- Selection sort
- Recursion
- Quicksort
- Hash tables
- Breadth-first search
- Dijkstra's algorithm
- Greedy algorithms
- Dynamic programming
- K-nearest neighbors
This accessible introduction is suitable for self-taught programmers, engineers, or anyone who wants to brush up on algorithms. Each carefully presented example includes helpful diagrams and fully annotated code samples in Python.
Aditya Bhargava is a software engineer with a dual background in computer science and fine arts. He blogs on programming at adit.io.
PLEASE NOTE: When you purchase this title, the accompanying PDF will be available in your Audible Library along with the audio.
- Listening Length3 hours and 46 minutes
- Audible release dateSeptember 27, 2018
- LanguageEnglish
- ASINB07HQYBNYK
- VersionUnabridged
- Program TypeAudiobook
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Product details
| Listening Length | 3 hours and 46 minutes |
|---|---|
| Author | Aditya Bhargava |
| Narrator | Derek Lettman |
| Audible.com Release Date | September 27, 2018 |
| Publisher | Manning Publications |
| Program Type | Audiobook |
| Version | Unabridged |
| Language | English |
| ASIN | B07HQYBNYK |
| Best Sellers Rank | #15,394 in Audible Books & Originals (See Top 100 in Audible Books & Originals) #3 in Data Science #7 in Machine Theory (Books) #8 in Programming & Software Development |
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I'm a 'seasoned' programmer, I would like to think (industry experience at a “top” company, CS degree, coding all my life, etc), and so almost all of this book is "review" for me. I'm going back through fundamentals in preparation for coding interviews, as I'm back on the market for a job. I'd say this book does these things very well:
1) Fills in the gaps that might've always been there - What I mean by this is, if you maybe got a bachelors in computer science, there could easily have been some material that just didn't completely sink in or that your curriculum didn't focus on. I think going through this book is a great way to make sure those gaps are filled.
2) Explains concepts in an easy to grasp form - The examples that are used in the book are great. There's one example early on with big O involving drawing 16 boxes on a piece of paper. One way is to draw 16 boxes one at a time - yielding O(n). Another way is to fold that piece of paper in half each time. This gets your 16 boxes in only 4 folds - big O(log n). It's simple, yet a great way of showing the difference in the two.
3) Keeps your attention - I love to buy books and then not read them… it’s a talent that I exercise often… One thing I can say about this book is that it actually keeps my attention, and I enjoy reading it. That’s saying a lot if it can do that. How much good is a book if it’s too boring to focus on and get through? If you don’t read it, it doesn’t matter how quality the content is.
I’ve recommended this book to several people in my life already, and I wish I still had my Amazon affiliate account setup because I feel like a freaking spokesperson for the thing..! Haha.
In summary: I would recommend this book to a very wide range of people—ANYONE in computer science looking to get a job, anyone trying to get a degree or just take anything CS related, anyone interested in some of the CS fundamentals, anyone looking to review computer science concepts, anyone wanting to dip their feet into a new field of study they haven’t explored before.
I wish I had this book when I was an undergrad—It would’ve saved me so much headache and difficulty.
I’ll update this review once I’ve finished more of the book, but from what I’ve gone through so far, and compared to plenty of other programming books (algorithms, interview prep books, etc), it’s by far my favorite.
From the author:
"Who should read this book?
This book is aimed at anyone who knows the basics of coding and wants to understand algorithms. Maybe you already have a coding problem and are trying to find an algorithmic solution. Or maybe you want to understand what algorithms are useful for. Here’s a short, incomplete list of people who will probably find this book useful:
• Hobbyist coders
• Coding boot camp students
• Computer science grads looking for a refresher
• Physics/math/other grads who are interested in programming"
In my opinion, the reason this book is so much better than traditional resources on the subject is that the author adheres to a few basic principles that I find fundamental to learning.
From the author:
"About this book
This book is designed to be easy to follow. I avoid big leaps of thought. Any time a new concept is introduced, I explain it right away or tell you when I’ll explain it. Core concepts are reinforced with exercises and multiple explanations so that you can check your assumptions and make sure you’re following along.
I lead with examples. Instead of writing symbol soup, my goal is to make it easy for you to visualize these concepts. I also think we learn best by being able to recall something we already know, and examples make recall easier. So when you’re trying to remember the difference between arrays and linked lists (explained in chapter 2), you can just think about getting seated for a movie. Also, at the risk of stating the obvious, I’m a visual learner. This book is chock-full of images.
The contents of the book are carefully curated. There’s no need to write a book that covers every sorting algorithm—that’s why we have Wikipedia and Khan Academy. All the algorithms I’ve included are practical. I’ve found them useful in my job as a so ware engineer, and they provide a good foundation for more complex topics."
The book starts with simple concrete examples and then moves towards abstraction. Most books on the subject start with abstract mathematical equations (symbol soup as the author calls it) and then move towards concrete implementations (or just stay with abstract proofs). That is approach is completely backwards to me, but perhaps it works well for others.























