解决经典的计算机科学问题，从基本算法，如排序和搜索，到机器学习和密码学中的现代算法
主要特征
更加重视现代深度学习技术，包括LSTM、GRU和RNN
较新的主题包括如何处理数据中隐藏的偏差以及算法的可解释性
探索不同的编程算法，并为它们的最佳实现选择正确的数据结构
书名
除了传统计算，使用算法解决现实世界问题的能力是任何开发人员或程序员必须具备的一项重要技能。这本书不仅能帮助你通过了解算法的工作原理，培养选择和使用算法解决现实世界问题的技能。
您将从算法介绍开始，了解各种算法设计技术，然后通过实例探索如何实现不同类型的算法，例如搜索和排序。当你学习一组更复杂的算法时，你将学习线性规划、页面排名和图表，甚至可以使用机器学习算法，理解它们背后的数学和逻辑。
天气预报、推特聚类和电影推荐引擎的案例研究将向您展示如何优化应用这些算法。然后你将关注深度学习算法，学习不同类型的深度学习模型及其实际应用。
最后，您将精通支持并行处理的技术，使您能够将这些算法用于计算密集型任务。
在这本编程书的末尾，你将熟练地使用各种算法来解决现实世界的计算问题。
你将学到什么
解决复杂问题的设计算法
熟悉神经网络和深度学习技术
探索Python库中现有的数据结构和算法
利用网络分析实现欺诈检测的图形算法
使用机器学习算法对类似推文进行聚类，并实时处理推文数据
创建一个向订阅者推荐相关电影的推荐引擎
在谷歌云平台上使用对称和非对称加密实现万无一失的安全性
这本书是给谁的
这本计算机科学书是为程序员或开发者编写的，他们想了解算法在解决问题和编写高效代码中的应用。
无论你是一个希望简明地学习最常用算法的初学者，还是一个希望探索数据科学、机器学习和密码学领域的前沿算法的经验丰富的程序员，你都会发现这本书很有用。
Python编程经验是必须的，数据科学知识会有所帮助，但不是必需的。
40 Algorithms Every Programmer Should Know – Second Edition (Early Access)
Solve classic computer science problems from fundamental algorithms, such as sorting and searching, to modern algorithms in machine learning and cryptography
Key Features
More emphasis on modern deep learning techniques, including LSTMs, GRUs, and RNNs
Newer topics include how to handle hidden bias in data and the explainability of the algorithms
Explore different programming algorithms and choose the right data structures for their optimal implementation
Book Description
Beyond traditional computing, the ability to use algorithms to solve real-world problems is an important skill that any developer or programmer must have. This book will help you not only to develop the skills to select and use an algorithm to tackle problems in the real world by understanding how it works.
You’ll start with an introduction to algorithms and discover various algorithm design techniques, before exploring how to implement different types of algorithms, such as searching and sorting, with the help of practical examples. As you advance to a more complex set of algorithms, you’ll learn about linear programming, page ranking, and graphs, and even work with machine learning algorithms, understanding the math and logic behind them.
Case studies on weather prediction, tweet clustering, and movie recommendation engines will show you how to apply these algorithms optimally. Then you will focus on deep learning algorithms and will learn about different types of deep learning models along with their practical use.
Finally, you’ll become well versed in techniques that enable parallel processing, giving you the ability to use these algorithms for compute-intensive tasks.
By the end of this programming book, you’ll have become adept at solving real-world computational problems by using a wide range of algorithms.
What you will learn
Design algorithms for solving complex problems
Become familiar with neural networks and deep learning techniques
Explore existing data structures and algorithms found in Python libraries
Implement graph algorithms for fraud detection using network analysis
Work with machine learning algorithms to cluster similar tweets and process Twitter data in real time
Create a recommendation engine that suggests relevant movies to subscribers
Implement foolproof security using symmetric and asymmetric encryption on Google Cloud Platform
Who This Book Is For
This computer science book is for programmers or developers who want to understand the use of algorithms for problem-solving and writing efficient code.
Whether you are a beginner looking to learn the most used algorithms concisely or an experienced programmer looking to explore cutting-edge algorithms in data science, machine learning, and cryptography, you’ll find this book useful.
Python programming experience is a must, knowledge of data science will be helpful but not necessary.
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