Java核心技术与Thinking in Java（Java编程思想）齐名的 Core Java。
2018年9月才出版的英文原版，包括了Java9，Java10，Java11的新特性，目前没有中文版，估计要很久才能出。卷一：基础知识（Volume I Fundamentals），卷二：高级特性（Volume II Advanced Features）。
书中囊括了Java的全部基础知识，提供了大量完整且具有实际意义的应用示例，详细介绍了Java语言基础、面向对象编程、反射与代理、接口与内部类、事件监听器模型、使用Swing GUI工具进行图形用户界面程序设计、打包应用程序、异常处理、登录与调试、泛型编程、集合框架、多线程、并发等内容。
In Learn Python 3 the Hard Way, Zed Shaw taught you the basics of Programming with Python 3. Now, in Learn More Python 3 the Hard Way, you’ll go far beyond the basics by working through 52 brilliantly crafted projects. Each one helps you build a key practical skill, combining demos to get you started and challenges to deepen your understanding. Zed then teaches you even more in 12 hours of online videos, where he shows you how to break, fix, and debug your code.
Mining of Massive Datasets by Jure Leskovec, Anand Rajaraman, Jeffrey David Ullman
Written by leading authorities in database and Web technologies, this book is essential reading for students and practitioners alike. The popularity of the Web and Internet commerce provides many extremely large datasets from which information can be gleaned by data mining. This book focuses on practical algorithms that have been used to solve key problems in data mining and can be applied successfully to even the largest datasets. It begins with a discussion of the map-reduce framework, an important tool for parallelizing algorithms automatically. The authors explain the tricks of locality-sensitive hashing and stream processing algorithms for mining data that arrives too fast for exhaustive processing. Other chapters cover the PageRank idea and related tricks for organizing the Web, the problems of finding frequent itemsets and clustering. This second edition includes new and extended coverage on social networks, machine learning and dimensionality reduction.
2017版。
Chapter 1, Python Objects, Types, and Expressions, introduces you to the basic types and objects of Python. We will give an overview of the language features, execution environment, and programming styles. We will also review the common programming techniques and language functionality.
Chapter 2, Python Data Types and Structures, explains each of the five numeric and five sequence data types, as well as one mapping and two set data types, and examine the operations and expressions applicable to each type. We will also give examples of typical use cases.
Chapter 3, Principles of Algorithm Design, covers how we can build additional structures with specific capabilities using the existing Python data structures. In general, the data structures we create need to conform to a number of principles. These principles include robustness, adaptability, reusability, and separating the structure from a function. We look at the role iteration plays and introduce recursive data structures.
Chapter 4, Lists and Pointer Structures, covers linked lists, which are one of the most common data structures and are often used to implement other structures, such as stacks and queues. In this chapter, we describe their operation and implementation. We compare their behavior to arrays and discuss the relative advantages and disadvantages of each.
Chapter 5, Stacks and Queues, discusses the behavior and demonstrates some implementations of these linear data structures. We give examples of typical applications.
Chapter 6, Trees, will look at how to implement a binary tree. Trees form the basis of many of the most important advanced data structures. We will examine how to traverse trees and retrieve and insert values. We will also look at how to create structures such as heaps.
Chapter 7, Hashing and Symbol Tables, describes symbol tables, gives some typical implementations, and discusses various applications. We will look at the process of hashing, give an implementation of a hash table, and discuss the various design considerations.
Chapter 8, Graphs and Other Algorithms, looks at some of the more specialized structures, including graphs and spatial structures. Representing data as a set of nodes and vertices is convenient in a number of applications, and from this, we can create structures such as directed and undirected graphs. We will also introduce some other structures and concepts such as priority queues, heaps, and selection algorithms.
[2] Preface
Chapter 9, Searching, discusses the most common searching algorithms and gives examples of their use for various data structures. Searching a data structure is a fundamental task and there are a number of approaches.
Chapter 10, Sorting, looks at the most common approaches to sorting. This will include bubble sort, insertion sort, and selection sort.
Chapter 11, Selection Algorithms, covers algorithms that involve finding statistics, such as the minimum, maximum, or median elements in a list. There are a number of approaches and one of the most common approaches is to first apply a sort operation. Other approaches include partition and linear selection.
Chapter 12, Design Techniques and Strategies, relates to how we look for solutions for similar problems when we are trying to solve a new problem. Understanding how we can classify algorithms and the types of problem that they most naturally solve is a key aspect of algorithm design. There are many ways in which we can classify algorithms, but the most useful classifications tend to revolve around either the implementation method or the design method.
Chapter 13, Implementations, Applications, and Tools, discusses a variety of real-world applications. These include data analysis, machine learning, prediction, and visualization. In addition, there are libraries and tools that make our work with algorithms more productive and enjoyable.
