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orb算法matlab代码资料夹说明 有许多计算机视觉的基本算法，可以在MATLAB或Python-OpenCV中实现。 可以下载或查阅它们以进行理解。 很少有开发的算法可以满足EE5731视觉计算课程（新加坡国立大学）的要求。 MATLAB文件夹：由PARTX表示 该文件夹可以保存到任何目录。 有六个partx_xx文件夹。 运行每个部分的步骤-a。 转到每个部分并解压缩vlfeat-0.9.21.rar文件夹。 该文件夹包含SIFT的代码，该代码已从下载-并被大量引用。 b。 Matlab的第一个工作路径应该是零件目录的设置。 为了让我运行part1代码，我需要将工作目录设置为- E:\\Assignment_1_Sayan_Kumar\Part1 Similarly to run the code for part2 change the MATLAB working directory to E:\\Assignment_1_Sayan_Kumar\Part2 b。 要运行每个部分，请运行以

2018年12月份，职业黑客、调查取证专家OccupyTheWeb出了一本书《Linux Basics for Hackers》，特别适合作为安全工作者入门信息安全的基础 Linux 学习和使用 目前亚马逊 Linux 书籍销售排行榜第 1 名，评分 4.4（满分5分）。 正如书名所言，本书是为那些没有Linux基础或经验的黑客/渗透测试人员准备了，但是如果你已经花了大量的时间在Linux身上，那么，这本书可能无法为你带来突破性的东西。此外，这本书还有一大特点，书中选用的不是Centilla或Debian发行版，而是Kali Linux，这是最受黑客欢迎的Linux发行版之一。

Deep Learning, Vol. 2: From Basics to Practice By 作者: Andrew Glassner Pub Date: 2018 ISBN: n/a Pages: (914 of 1750) Format: PDF Publication Date: February 19, 2018 Language: English ASIN: B079Y1M81K People are using the tools of deep learning to change how we think about science, art, engineering, business, medicine, and even music. This book is for people who want to understand this field well enough to create deep learning systems, train them, and then use them with confidence to make their own contributions. The book takes a friendly, informal approach. Our goal is to make the ideas of this field simple and accessible to everyone, as shown in the Contents below. Since most practitioners today use one of several free, open-source deep-learning libraries to build their systems, the hard part isn’t in the programming. Rather, it’s knowing what tools to use, and when, and how. Building a working deep learning system requires making a series of technically informed choices, and with today’s tools, those choices require understanding what’s going on under the hood. This book is designed to give you that understanding. You’ll be able to choose the right kind of architecture, how to build a system that can learn, how to train it, and then how to use it to accomplish your goals. You’ll be able to read and understand the documentation for whatever library you’d like to use. And you’ll be able to follow exciting, on-going breakthroughs as they appear, because you’ll have the knowledge and vocabulary that let you read new material, and discuss it with other people doing deep learning. The book is extensively illustrated with over 1000 original figures. They are also all available for free download, for your own use. You don’t need any previous experience with machine learning or deep learning for this book. You don’t need to be a mathematician, because there’s nothing in the book harder than the occasional multiplication. You don’t need to choose a particular programming language, or library, or piece of hardware, because our approach is largely independent of those things. Our focus is on the principles and techniques that are applicable to any language, library, and hardware. Even so, practical programming is important. To stay focused, we gather our programming discussions into 3 chapters that show how to use two important and free Python libraries. Both chapters come with extensive Jupyter notebooks that contain all the code. Other chapters also offer notebooks for for every Python-generated figure. Our goal is to give you all the basics you need to understand deep learning, and then show how to use those ideas to construct your own systems. Everything is covered from the ground up, culminating in working systems illustrated with running code. The book is organized into two volumes. Volume 1 covers the basic ideas that support the field, and which form the core understanding for using these methods well. Volume 2 puts these principles into practice. Deep learning is fast becoming part of the intellectual toolkit used by scientists, artists, executives, doctors, musicians, and anyone else who wants to discover the information hiding in their data, paintings, business reports, test results, musical scores, and more. This friendly, informal book puts those tools into your pocket.

This is one of my most favorite books that teache deep learning from the very basic to advance level. The book does not emphasize on mathematics but explains the logic behind each deep learning algorithm in plain English. Even if you are the new guy in deep learning or you are already at an advance level, this book is for you. Vol 2 will be coming shortly