"Algorithm graphic" All this PDF download link attached Baidu Cloud

The author uses Python and pictures to explain the algorithm, looking for a long time to find the PDF version, the end of the link attached Baidu Cloud ~

Graphic algorithm

On the [US] Aditya Bhargava

Translator Yuan Guozhong

Category Publishing / non-fiction

Press Posts & Telecom Press / 2017-03

Provider Turing community

Words about 72,000 words

. ISBN 9787115447630

introduction

This book example is rich, with illustrations to explain the way makes it easier to understand the algorithm, designed to help programmers better play algorithm in daily energy projects. The first three chapters of the book will help you lay the foundation, take you to learn binary search, Big O notation, two basic data structures and recursion. The remaining space will focus on the widely used algorithms specifically includes: solving skills when faced with specific problems, such as when a greedy algorithm or dynamic programming; use a hash table; graph algorithms; K nearest neighbor algorithm.

Aditya Bhargava, software engineer, both computer science and fine arts education, wrote in a blog programming adit.io.

List of works

Foreword
Acknowledgments
About the Book
Chapter 1 Introduction to Algorithms
1.1 Introduction
1.2 Binary Search
1.3 Big O notation
1.4 Summary
Chapter 2 Selection Sort
2.1 Memory Works
2.2 arrays and linked lists
2.3 Selection Sort
2.4 Summary
Chapter 3 Recursion
3.1 Recursion
3.2 baseline conditions and recursion conditions
3.3 Stack
3.4 Summary
Chapter 4 Quick Sort
4.1 Divide and conquer
4.2 Quick Sort
4.3 talk about the big O notation
4.4 Summary
Chapter 5 hash table
5.1 hash function
5.2 Applications
5.3 Conflict
5.4 Performance
5.5 Summary
Chapter 6 BFS
Figure 6.1 Introduction
Figure 6.2 What is
6.3 breadth-first search
FIG implemented 6.4
6.5 algorithm
6.6 Summary
Chapter 7 Dijkstra algorithm
7.1 Dijkstra algorithm
7.2 The term
7.3 In other piano
7.4 negative right side
7.5 realized
7.6 Summary
Chapter 8 greedy algorithm
8.1 classroom scheduling problem
8.2 knapsack problem
8.3 set covering problem
8.4 NP-complete problems
8.5 Summary
Chapter 9 Dynamic Programming
9.1 knapsack problem
9.2 knapsack problem FAQ
9.3 longest common substring
9.4 Summary
Chapter 10 K-nearest neighbor algorithm
10.1 orange or grapefruit
10.2 Creating recommendation system
10.3 Introduction to Machine Learning
10.4 Summary
Chapter 11 How to do next
11.1 Tree
11.2 inverted index
11.3 Fourier transform
11.4 Parallel Algorithms
11.5　MapReduce
11.6 Bloom filter and HyperLogLog
11.7 SHA algorithm
11.8 locality sensitive hashing algorithm
Key exchange 11.9 Diffie-Hellman
11.10 Linear Programming
11.11 Conclusion
To exercises

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