Machine Learning: A Probabilistic Perspective (Adaptive Computation and Machine Learning series) Today’s Web-enabled deluge of electronic data calls for automated methods of data analysis. Machine learning provides these, developing methods that can automatically detect patterns in data and then use the uncovered patterns to predict future data. This textbook offers a comprehensive and self-contained introduction to the field of machine learning, based on a unified, probabilistic approach. The coverage combines breadth and depth, offering necessary background material on such topics as probability, optimization, and linear algebra as well as discussion of recent developments in the field, including conditional random fields, L1 regularization, and deep learning. The book is written in an informal, accessible style, complete with pseudo-code for the most important algorithms. All topics are copiously illustrated with color images and worked examples drawn from such application domains as biology, text processing, computer vision, and robotics. Rather than providing a cookbook of different heuristic methods, the book stresses a principled model-based approach, often using the language of graphical models to specify models in a concise and intuitive way. Almost all the models described have been implemented in a MATLAB software package–PMTK (probabilistic modeling toolkit)–that is freely available online. The book is suitable for upper-level undergraduates with an introductory-level college math background and beginning graduate students. 标题和描述中提到的知识点可以细化为以下几点： 1. 机器学习的定义和重要性：机器学习是自动化数据分析的方法，能够自动检测数据中的模式，并利用这些模式预测未来的数据。这门技术是应对今天网络上电子数据激增的有效手段。 2. 统计模型和概率方法：本书强调基于概率的机器学习方法。这意味着机器学习模型通常会通过概率论的语言来描述和推断数据中的关系。 3. 机器学习的基本组成部分：包括概率论、优化方法和线性代数等基础知识。这些是构建和理解机器学习算法的基础。 4. 最新机器学习技术：书中介绍了若干最近的机器学习领域的发展，例如条件随机场（Conditional Random Fields）、L1正则化（L1 Regularization）和深度学习（Deep Learning）。 5. 机器学习的应用示例：在介绍理论的同时，书中使用了大量彩色图像和实际应用案例，帮助读者理解算法在生物信息学、文本处理、计算机视觉和机器人技术等领域的应用。 6. 模型驱动的方法：作者提倡使用基于原理的模型驱动方法，这通常涉及到图形模型（Graphical Models），通过图形模型来简洁直观地指定模型。 7. 编程实践和MATLAB软件包：本书不仅讨论理论，还提供了模型的MATLAB实现。这些模型已经包含在PMTK（概率建模工具包）软件包中，该软件包可以在网上免费获取。 8. 教育适用性：这本书适合已经具备基础大学数学背景的高年级本科生和初学者研究生。 9. 作者背景：Kevin P. Murphy是谷歌的研究科学家，并且曾经是不列颠哥伦比亚大学的计算机科学和统计学副教授。 10. 书籍评价：书籍得到了同行的广泛认可，被认为是一本直觉性强、内容丰富、易于理解但又全面深入的教材。它适合于大学学生学习，并且是机器学习领域从业者的必备书籍。 从上述内容可以看出，《Machine Learning: A Probabilistic Perspective》是一本全面介绍概率视角下机器学习方法的教科书。它不仅提供了机器学习基础理论的介绍，还包括了用于实践的算法伪代码以及在不同领域应用的例子。该书强调理论与实践相结合，注重原理模型的构建，并配有相应的编程实践，帮助读者能够更好地理解和运用机器学习技术。

经典计算机操作系统教材第三版，详细内容可见亚马逊。 https://www.amazon.com/Computer-Systems-Programmers-Perspective-Engineering/dp/0134123832/ref=sr_1_2?ie=UTF8&qid=1541476471&sr=8-2&keywords=computer+systems+a+programmer's+perspective Computer systems: A Programmer’s Perspective explains the underlying elements common among all computer systems and how they affect general application performance. Written from the programmer’s perspective, this book strives to teach readers how understanding basic elements of computer systems and executing real practice can lead them to create better programs. Spanning across computer science themes such as hardware architecture, the operating system, and systems software, the Third Edition serves as a comprehensive introduction to programming. This book strives to create programmers who understand all elements of computer systems and will be able to engage in any application of the field--from fixing faulty software, to writing more capable programs, to avoiding common flaws. It lays the groundwork for readers to delve into more intensive topics such as computer architecture, embedded systems, and cyber security. This book focuses on systems that execute an x86-64 machine code, and recommends that programmers have access to a Linux system for this course. Programmers should have basic familiarity with C or C++. Personalize Learning with MasteringEngineering MasteringEngineering is an online homework, tutorial, and assessment system, designed to improve results through personalized learning. This innovative online program emulates the instructor’s office hour environment, engaging and guiding students through engineering concepts with self-paced individualized coaching With a wide range of activities available, students can actively learn, understand, and retain even the most difficult concepts.

影像透视变换-图片透视变换（投影变换） python处理图片，包括图片平移，图片旋转，图片缩放，图片倾斜，透视变换。选择图片中的四个关键点和将要变换的点，用于生成新的透视图 使用平移，缩放，翻转，旋转将一张图片转换为多张图片。 参考链接： : 主框架用的这位大佬的代码，我加了透视变换和鼠标交互的功能。

《Machine Learning_ A Bayesian and Optimization Perspective》 作者：Sergios Thedoridis

Computer Systems_A Programmer’s Perspective, 3rd Edition 英文版

Machine Learning A Bayesian and Optimization Perspective.pdf

CSAPP 第二版(Computer.Systems：A.Programmer's.Perspective)[英文原版]

这是计算机系统领域的经典著作（英文版），程序员修炼内功的必读书目。

Digital Integrated Circuits: A design Perspective, J.M.Rabaey, Prentice Hall 2003 国外数字集成电路设计经典教材，英文版

MBTI透视测试 该项目是一个示例项目，演示了MBTI透视测试。 它旨在作为DevOps工程师演示各种部署解决方案。 运行应用程序N DOCKER容器 挑战的解决方案已编码为Node.js应用程序，该应用程序导出/result API以提交mbti测试成绩，并且用户对每个问题的响应都将存储在PostgreSQL数据库中。 前端是一个Simple React应用程序，它为用户提供了进行MBTI测试并查看他们在MBTI维度上得分的界面。 另外，将结果提交到后端API进行存储。 这两个应用程序可以独立运行并通过导出API /result进行通信，但为​​方便起见，我还公开了/ API为前端提供index.html文件（在运行npm run build后可用）。 笔记： 为了方便设置来测试应用程序， .env文件已包含在内，用于提供到postgres数据库服务器的连接字符串。 如果在本地运