Grokking Algorithms 2nd Edition by Aditya Bhargava ISBN 1638354561 9781638354567

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Product details:

ISBN 10: 1638354561

ISBN 13: 978-1638354567

Author: Aditya Y. Bhargava

Grokking Algorithms 2nd Edition: A friendly, fully-illustrated introduction to the most important computer programming algorithms.

Master the most widely used algorithms and be fully prepared when you’re asked about them at your next job interview. With beautifully simple explanations, over 400 fun illustrations, and dozens of relevant examples, you’ll actually enjoy learning about algorithms with this fun and friendly guide!

In Grokking Algorithms, Second Edition you will discover:

• Search, sort, and graph algorithms
• Data structures such as arrays, lists, hash tables, trees, and graphs
• NP-complete and greedy algorithms
• Performance trade-offs between algorithms
• Exercises and code samples in every chapter
• Over 400 illustrations with detailed walkthroughs

The first edition of Grokking Algorithms proved to over 100,000 readers that learning algorithms doesn’t have to be complicated or boring! This revised second edition contains brand new coverage of trees, including binary search trees, balanced trees, B-trees and more. You’ll also discover fresh insights on data structure performance that takes account of modern CPUs. Plus, the book’s fully annotated code samples have been updated to Python 3.
Foreword by Daniel Zingaro.
Purchase of the print book includes a free eBook in PDF and ePub formats from Manning Publications.
About the technology
The algorithms you use most often have already been discovered, tested, and proven. Grokking Algorithms, Second Edition makes it a breeze to learn, understand, and use them. With beautifully simple explanations, over 400 fun illustrations, and dozens of relevant examples, it’s the perfect way to unlock the power of algorithms in your everyday work and prepare for your next coding interview—no math required!
About the book
Grokking Algorithms, Second Edition teaches you important algorithms to speed up your programs, simplify your code, and solve common programming problems. Start with tasks like sorting and searching, then build your skills to tackle advanced problems like data compression and artificial intelligence. You’ll even learn to compare the performance tradeoffs between algorithms. Plus, this new edition includes fresh coverage of trees, NP-complete problems, and code updates to Python 3.
What’s inside

• Search, sort, and graph algorithms
• Data structures such as arrays, lists, hash tables, trees, and graphs
• NP-complete and greedy algorithms
• Exercises and code samples in every chapter

About the reader

No advanced math or programming skills required.

About the author

Aditya Bhargava is a Software Engineer with a dual background in Computer Science and Fine Arts. He blogs on programming at adit.io.

 

Grokking Algorithms 2nd Edition Table of contents:

1 Introduction to algorithms

• Binary search
• Big O notation
• Recap

2 Selection sort

• How memory works
• Arrays and linked lists
• Which is used more, arrays or linked lists?
• Selection sort
• Example code listing
• Recap

3 Recursion

• Recursion
• Base case and recursive case
• The stack
• Recap

4 Quicksort

• Divide and conquer
• Quicksort
• Big O notation revisited
• Recap

5 Hash tables

• Hash functions
• Use cases
• Collisions
• Performance
• Recap

6 Breadth-first search

• Introduction to graphs
• What is a graph?
• Breadth-first search
• Implementing the graph
• Implementing the algorithm
• Recap

7 Trees

• Your first tree
• A space odyssey: Depth-first search
• Binary trees
• Huffman coding
• Recap

8 Balanced trees

• A balancing act
• Shorter trees are faster
• AVL trees: A type of balanced tree
• Splay trees
• B-trees
• Recap

9 Dijkstra’s algorithm

• Working with Dijkstra’s algorithm
• Terminology
• Trading for a piano
• Negative-weight edges
• Implementation
• Recap

10 Greedy algorithms

• The classroom scheduling problem
• The knapsack problem
• The set-covering problem
• Recap

11 Dynamic programming

• The knapsack problem (revisited)
• Knapsack problem FAQ
• Longest common substring
• Recap

12 k-nearest neighbors

• Classifying oranges vs. grapefruit
• Building a recommendations system
• Regression
• Introduction to machine learning
• A high-level overview of training an ML model
• Recap

13 Where to go next

• Linear regression
• Inverted indexes
• The Fourier transform
• Parallel algorithms
• map/reduce
• Bloom filters and HyperLogLog
• HTTPS and the Diffie–Hellman key exchange
• Locality-sensitive hashing
• Min heaps and priority queues
• Linear programming
• Epilogue

 

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