802.11® Wireless Networks The Definitive Guide, Second Edition .pdf 英文版，经典

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

Summit 300-48交换机是一种48端口（外加四个铜缆端口和迷你型GBIC端口）第二层/第三层WLAN交换机，它带有一个扩展槽，可以用于未来升级和新兴的移动应用。Summit 300-48交换机带有冗余电源，为无线端口提供了全面的加密和安全服务，如AES、WES和WPA。为支持这一解决方案而增强的ExtremeWare 操作系统功能包括改善的安全性、扩充能力和透明管理功能。除Extreme的Altitude 300无线端口外，Summit 300-48基本上可以与符合标准的其它无线接入点一起运行。

内含Jeff Heaton的三本关于神经网络的英文书： （1）《Introduction to Neural Networks for Java, 2nd Edition》； （2）《Introduction to neural networks for c# Second Edtion》； （3）《Introduction to the Math of Neural Network》

城市交通网络中的随机混合均衡行为建模，赵晖，高自友，本文研究了随机系统中同时包含竞争行为与合作行为的混合交通平衡。在目标系统中，同时考虑三类不同性质的局中人，即具有随机用户

Learning Generative Adversarial Networks 英文无水印pdf pdf所有页面使用FoxitReader和PDF-XChangeViewer测试都可以打开 本资源转载自网络，如有侵权，请联系上传者或csdn删除 本资源转载自网络，如有侵权，请联系上传者或csdn删除

计算机网络经典 附习题答案 英文版 原书为chm格式，答案为pdf格式

NetScreen-200 系列产品带有 4 个或 8 个具有自动速率检测、两极自动调整功能的 10/100 Base-T 以太网端口,能够提供接近线速的防火墙功能(NetScreen-204 的速度为 400 Mbps, NetScreen-208 的速度为 550 Mbps)。即使 3DES 和 AES 加密等严格应用上亦能提供高于 200 Mbps 的速率性能。

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.

总共1000多页，很好的资料，着重讲DL4J。