生成绘画火炬 根据作者的，对PyTorch重新。 先决条件 该代码已经在Ubuntu 14.04上进行了测试，以下是需要安装的主要组件： Python3 PyTorch 1.0+ 火炬视觉0.2.0+ 张量板 pyyaml 训练模型 python train.py --config configs/config.yaml 检查点和日志将保存到checkpoints 。 用训练好的模型进行测试 默认情况下，它将在检查点中加载最新保存的模型。 您也可以使用--iter通过迭代选择保存的模型。 训练有素的PyTorch模型：[ ] [] python test_single.py \ --image examples/imagenet/imagenet_patches_ILSVRC2012_val_00008210_input.png \ --mask examples/cen

A step-by-step gentle journey through the mathematics of neural networks, and making your own using the Python computer language. Neural networks are a key element of deep learning and artificial intelligence, which today is capable of some truly impressive feats. Yet too few really understand how neural networks actually work. This guide will take you on a fun and unhurried journey, starting from very simple ideas, and gradually building up an understanding of how neural networks work. You won't need any mathematics beyond secondary school, and an accessible introduction to calculus is also included. The ambition of this guide is to make neural networks as accessible as possible to as many readers as possible - there are enough texts for advanced readers already! You'll learn to code in Python and make your own neural network, teaching it to recognise human handwritten numbers, and performing as well as professionally developed networks. Part 1 is about ideas. We introduce the mathematical ideas underlying the neural networks, gently with lots of illustrations and examples. Part 2 is practical. We introduce the popular and easy to learn Python programming language, and gradually builds up a neural network which can learn to recognise human handwritten numbers, easily getting it to perform as well as networks made by professionals. Part 3 extends these ideas further. We push the performance of our neural network to an industry leading 98% using only simple ideas and code, test the network on your own handwriting, take a privileged peek inside the mysterious mind of a neural network, and even get it all working on a Raspberry Pi. All the code in this has been tested to work on a Raspberry Pi Zero.

对最初网络的一份描述性文档。是网络中的圣经，网络由此产生

☆ 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