Software.Engineering_A.Practitioner's. Approach,8th.Edition，英文版，非扫描版 软件工程 实践者的研究方法 第8版 英文版，含目录 超清文字版 非扫描版 完整pdf 文字，图都超清晰，书本文字内容可选定复制

书是英文原版，里面有源码，很清楚，比市面上其他书好太多，以动手编程为导向，而不是各种抽象概念。用最简单基本的方法，实现C++的快速入门，文件夹中有部分更新的文件以及源码

C How to Program is a comprehensive introduction to programming in C. Like other texts of the Deitels’ How to Program series, the book serves as a detailed beginner source of information for college students looking to embark on a career in coding, or instructors and software-development professionals seeking to learn how to program with C.

electric class, 英文版的，有兴趣就看一下

A FIRST COURSE IN PROBABILITY Eighth Edition Sheldon Ross University of Southern California

本书是Java语言的经典教材，中文版分为基础篇和进阶篇，主要介绍程序设计基础、面向对象编程、GUI程序设计、数据结构和算法、高级Java程序设计等内容。本书以示例讲解解决问题的技巧，提供大量的程序清单，每章配有大量复习题和编程练习题，帮助读者掌握编程技术，并应用所学技术解决实际应用开发中遇到的问题。您手中的这本是其中的基础篇，主要介绍了基本程序设计、语法结构、面向对象程序设计、继承和多态、异常处理和文本I/O、抽象类和接口等内容。本书可作为高等院校程序设计相关专业的基础教材，也可作为Java语言及编程开发爱好者的参考资料。 作者：（美国）粱勇（Y.Daniel Liang） 译者：戴开宇 梁勇（Y.Daniel Liang），现为阿姆斯特朗亚特兰大州立大学计算机科学系教授。之前曾是普度大学计算机科学系副教授。并两次获得普度大学杰出研究奖。他所编写的Java教程在美国大学Java课程中采用率极高。同时他还兼任Prentice Hall Java系列丛书的编辑。他是“Java Champion”荣誉得主，并在世界各地为在校学生和程序员做JAVA程序设计方法及技术方面的讲座。 戴开宇，复旦大学软件学院教师，工程硕士导师。中国计算机学会会员。博士毕业于上海交通大学计算机应用专业，2011～2012年在美国佛罗里达大学作访问学者。承担多门本科专业课程、通识教育课程以及工程硕士课程，这些课程被评为校精品课程，上海市重点建设课程，IBM—教育部精品课程等。

Core.Java.Volume.II.Advanced.Features,8th.Edition.chm java核心第8版第二卷

for Complex Variables and Applications

A First Course In Probability 8th

Elementary Linear Algebra, 8th Edition by Ron Larson | 467 pages Table of Contents 1. SYSTEMS OF LINEAR EQUATIONS. Introduction to Systems of Equations. Gaussian Elimination and Gauss-Jordan Elimination. Applications of Systems of Linear Equations. 2. MATRICES. Operations with Matrices. Properties of Matrix Operations. The Inverse of a Matrix. Elementary Matrices. Markov Chains. Applications of Matrix Operations. 3. DETERMINANTS. The Determinant of a Matrix. Evaluation of a Determinant Using Elementary Operations. Properties of Determinants. Applications of Determinants. 4. VECTOR SPACES. Vectors in Rn. Vector Spaces. Subspaces of Vector Spaces. Spanning Sets and Linear Independence. Basis and Dimension. Rank of a Matrix and Systems of Linear Equations. Coordinates and Change of Basis. Applications of Vector Spaces. 5. INNER PRODUCT SPACES. Length and Dot Product in Rn. Inner Product Spaces. Orthogonal Bases: Gram-Schmidt Process. Mathematical Models and Least Squares Analysis. Applications of Inner Product Spaces. 6. LINEAR TRANSFORMATIONS. Introduction to Linear Transformations. The Kernel and Range of a Linear Transformation. Matrices for Linear Transformations. Transition Matrices and Similarity. Applications of Linear Transformations. 7. EIGENVALUES AND EIGENVECTORS. Eigenvalues and Eigenvectors. Diagonalization. Symmetric Matrices and Orthogonal Diagonalization. Applications of Eigenvalues and Eigenvectors. 8. COMPLEX VECTOR SPACES (online). Complex Numbers. Conjugates and Division of Complex Numbers. Polar Form and Demoivre’s Theorem. Complex Vector Spaces and Inner Products. Unitary and Hermitian Spaces. 9. LINEAR PROGRAMMING (online). Systems of Linear Inequalities. Linear Programming Involving Two Variables. The Simplex Method: Maximization. The Simplex Method: Minimization. The Simplex Method: Mixed Constraints. 10. NUMERICAL METHODS (online). Gaussian Elimination with Partial Pivoting. Iterative Methods for Solving Linear Systems. Power Method for Approximating Eigenvalues. Applications of Numerical Methods.