☆ 资源说明：☆ [奥莱理] Java 网络编程 第4版 (英文版) [奥莱理] Java Network Programming 4th Edition (E-Book) ☆ 图书概要：☆ This practical guide provides a complete introduction to developing network programs with Java. You’ll learn how to use Java’s network class library to quickly and easily accomplish common networking tasks such as writing multithreaded servers, encrypting communications, broadcasting to the local network, and posting data to server-side programs. Author Elliotte Rusty Harold provides complete working programs to illustrate the methods and classes he describes. This thoroughly revised fourth edition covers REST, SPDY, asynchronous I/O, and many other recent technologies. Explore protocols that underlie the Internet, such as TCP/IP and UDP/IP Learn how Java’s core I/O API handles network input and output Discover how the InetAddress class helps Java programs interact with DNS Locate, identify, and download network resources with Java’s URI and URL classes Dive deep into the HTTP protocol, including REST, HTTP headers, and cookies Write servers and network clients, using Java’s low-level socket classes Manage many connections at the same time with the nonblocking I/O ☆ 出版信息：☆ [作者信息] Elliotte Rusty Harold [出版机构] 奥莱理 [出版日期] 2013年10月14日 [图书页数] 502页 [图书语言] 英语 [图书格式] PDF 格式

oost.Asio C++ Network Programming Cookbook is filled with real-world problems related to network programming that show the Boost.Asio library in motion.

中文版和英文版 非常难找到的 答：狗能携带21 千兆字节或者168千兆位的数据。18 公里/小时的速度等于0.005 公里/秒，走过x 公里的时 间为x / 0.005 = 200x 秒， 产生的数据传输速度为168/200x Gbps或者840 /x Mbps。因此，与通信线路相比较，若x<5.6 公里，狗有更高的速度。 SOLUTIONS TO CHAPTER 1 PROBLEMS 1. The dog can carry 21 gigabytes, or 168 gigabits. A speed of 18 km/hour equals 0.005 km/sec. The time to travel distance x km is x /0.005 = 200x sec, yielding a data rate of 168/200x Gbps or 840/x Mbps. For x < 5.6 km, the dog has a higher rate than the communication line.

墨西哥帽子matlab代码神经网络算法 用MATLAB编写的神经网络算法 hebbian.m 该代码采用输入向量，权重，学习常数，并在每个阶段绘制更新后的权重 净额 代码将两个矩阵相乘 BAM_network.m 这个Matlab代码在以5x3的矩阵制作时为英语alphabects训练了双向联想存储网络的权重。 max_net.m 基于竞争的神经网络的具体示例。 可以用作子网来选择输入量最大的节点。 max_hat.m 该matlab代码采用以下参数输入n个输入神经元：->互连区域的半径->具有正互连的区域的半径->恒定c1->恒定c2->外部信号。 该代码对这些输入神经元执行墨西哥帽算法，并执行所需的次数。 hamming_net.m 这些网络可用于查找最接近双极性输入向量x的示例。 索姆 此代码已演示了Kohonen自组织图，也称为拓扑保留图算法。 lvq.m 该代码显示了线性向量量化算法的工作原理。 目前，代码将2类分类。 将对代码进行进一步的改进。 感知器 该代码显示了用于逻辑门的感知器学习算法的实现。 在最初阶段，已实现了“与门”，其输入值和目标输出可在代码中轻松修改。 它采

LazyProgrammer, "Convolutional Neural Networks in Python: Master Data Science and Machine Learning with Modern Deep Learning in Python, Theano, and TensorFlow" 2016 | ASIN: B01FQDREOK | 52 pages | EPUB | 1 MB This is the 3rd part in my Data Science and Machine Learning series on Deep Learning in Python. At this point, you already know a lot about neural networks and deep learning, including not just the basics like backpropagation, but how to improve it using modern techniques like momentum and adaptive learning rates. You've already written deep neural networks in Theano and TensorFlow, and you know how to run code using the GPU. This book is all about how to use deep learning for computer vision using convolutional neural networks. These are the state of the art when it comes to image classification and they beat vanilla deep networks at tasks like MNIST. In this course we are going to up the ante and look at the StreetView House Number (SVHN) dataset - which uses larger color images at various angles - so things are going to get tougher both computationally and in terms of the difficulty of the classification task. But we will show that convolutional neural networks, or CNNs, are capable of handling the challenge! Because convolution is such a central part of this type of neural network, we are going to go in-depth on this topic. It has more applications than you might imagine, such as modeling artificial organs like the pancreas and the heart. I'm going to show you how to build convolutional filters that can be applied to audio, like the echo effect, and I'm going to show you how to build filters for image effects, like the Gaussian blur and edge detection. After describing the architecture of a convolutional neural network, we will jump straight into code, and I will show you how to extend the deep neural networks we built last time with just a few new functions to turn them into CNNs. We will then test their performance and show how convolutional neural networks written in both Theano and TensorFlow can outperform the accuracy of a plain neural network on the StreetView House Number dataset.

经典linux网络应用，在美国很流行的一本教材。

Advanced Linux Networking

USB over Network 6.0.21最新版全版本客户端（Windows、Linux、ARM、MIPS），官网只提供服务端下载（server），不提供Linux版本的客户端下载（client）。ftvusbnet是USB_over_Network系列工具在linux下的client端。

DOS Network Technical Whitepaper英文版白皮书，DOS Network支持多条主流公链的去中心化预言机服务网络

17.1 在线监控 17.1.1 切换主机的运转/停止状态 在进行测试与除错的过程中，须要经常性地改变主机的运行状态，而透过 ISPSoft，我们可轻易的进 行切换。在操作前，请确认目前 ISPSoft已可与主机正常联机，详细说明请参考第 2.4节。 于功能工具栏中点选 PLC 主机（P） > 运行（R），或按下图示工具栏的 图标可将主机切换至 RUN 的状态；而于功能工具栏中点选 PLC主机（P） > 停止（S），或按下图示工具栏的 图标 则可重新将主机切换至 STOP状态。 透过 ISPSoft 来切换主机的状态时，并不用考虑主机本体的 RUN/STOP 开关位置；且由 ISPSoft 下 达 RUN/STOP命令后，若再次切换主机本体的 RUN/STOP开关时还是可以变更主机的运行状态。 17.1.2 在线监控的功能与环境介绍 当 ISPSoft已可与主机正常联机后，我们便可经由在线监控模式来对 PLC 的执行状况进行监控。关于 主机与 ISPSoft之间的联机设定方式请参考第 2.4 节的内容。 在 ISPSoft中，在线监控的模式又可分为「装置监控」与「程序监控」。 监控模式 说明 装置监控 可透过监控表来实时监控主机目前的装置状态，且在此模式下，ISPSoft仅需更 新装置状态，因此目前 ISPSoft所开启的程序与主机内部的程序无须一致。 程序监控 在此模式下，系统会实时将程序的运作状况显示于程序画面中，也因此系统会要 求目前 ISPSoft所开启的程序必须与主机内部的程序一致。 *.装置监控模式可单独启动，而程序监控模式则必须伴随装置监控模式一起启动。