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Python hacker. Those are two words you really could use to describe me. At Immunity, I am lucky enough to work with people who actually, really, know how to code Python. I am not one of those people. I spend a great deal of my time penetration testing, and that requires rapid Python tool development, with a focus on execution and delivering results (not necessarily on prettiness, optimization, or even stability). Throughout this book you will learn that this is how I code, but I also feel as though it is part of what makes me a strong pentester. I hope that this philosophy and style helps you as well. As you progress through the book, you will also realize that I don’t take deep dives on any single topic. This is by design. I want to give you the bare minimum, with a little flavor, so that you have some foundational knowl- edge. With that in mind, I’ve sprinkled ideas and homework assignments throughout the book to kickstart you in your own direction. I encourage you to explore these ideas, and I would love to hear back any of your own implementations, tooling, or homework assignments that you have done. xviii Preface As with any technical book, readers at different skill levels with Python (or information security in general) will experience this book differently. Some of you may simply grab it and nab chapters that are pertinent to a consulting gig you are on, while others may read it cover to cover. I would recommend that if you are a novice to intermediate Python programmer that you start at the beginning of the book and read it straight through in order. You will pick up some good building blocks along the way. To start, I lay down some networking fundamentals in Chapter 2 and slowly work our way through raw sockets in Chapter 3 and using Scapy in Chapter 4 for some more interesting network tooling. The next section of the book deals with hacking web applications, starting with your own custom tooling in Chapter 5 and then extending the popular Burp Suite in Ch

这是一本关于天文学方面的数据分析参考指导书。书中使用了Python和R语言作为主要编程语言。探讨了几种基本线性模型及常用的一些天文学相关知识。对天文学有所热爱的同学不妨尝试一下。

欢迎使用机器学习的线性代数基础知识。线性代数是机器学习的支柱。 该领域在大约150年前开始正式化，但仅仅在大约70年前，现代线性代数才出现。这是一个巨大的研究领域，已经对数学的其他领域产生了影响，例如统计学，以及工程和物理学。值得庆幸的是，我们不需要知道线性代数领域的广度和深度，以便提高我们对机器学习的理解和应用。

计算机系统架构；Architecture；课程讲义；辅导书；参考书籍