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包含华为eNSP和VisualBox和winPcap和VMcomputer1

对抗生成网络学习（十二）——MARTA-GAN实现遥感图像的场景生成(tensorflow实现)-附件资源

用于网络学习的真实网络science，希望能对大家有用，里面为gml文件

基于卷积神经网络的手写体识别19.2 深度学习的概念及关键技术1、神经网络模型2、深度学习之卷积神经网络19.3 Python深度学习库——Keras1、Keras的安装2、Keras的网络层3、用Keras构建神经网络19.4 程序设计的思路19.5 源代码1、MNIST数据集2、手写体识别案例体现3、预测自己手写图像 #!/usr/bin/env python # coding: utf-8 19.2 深度学习的概念及关键技术 1、神经网络模型 ·生物神经元 ·轴突（输出）终端为其他神经元的树突（输入） ·人工神经网络 2、深度学习之卷积神经网络 ·深度学习结构：含有多隐层的神经网络。

无线自组织网络系统学习的好资料，该资料里是PPT课件，对才接触该类型网络的童鞋有很大帮助

来自MIT大佬Natalie Enright Jerger、Tushar Krishna、以及Li-Shiuan Peh总结的体系结构片上网络（onchip network）的学习材料（目前主要用于MIT相关课程教学）。 英文摘要：This book targets engineers and researchers familiar with basic computer architecture concepts who are interested in learning about on-chip networks. This work is designed to be a short synthesis of the most critical concepts in on-chip network design. It is a resource for both understanding on-chip network basics and for providing an overview of state of-the-art research in on-chip networks. We believe that an overview that teaches both fundamental concepts and highlights state-of-the-art designs will be of great value to both graduate students and industry engineers. While not an exhaustive text, we hope to illuminate fundamental concepts for the reader as well as identify trends and gaps in on-chip network research. With the rapid advances in this field, we felt it was timely to update and review the state of the art in this second edition. We introduce two new chapters at the end of the book. We have updated the latest research of the past years throughout the book and also expanded our coverage of fundamental concepts to include several research ideas that have now made their way into products and, in our opinion, should be textbook concepts that all on-chip network practitioners should know. For example, these fundamental concepts include message passing, multicast routing, and bubble flow control schemes. Table of Contents: Preface / Acknowledgments / Introduction / Interface with System Architecture / Topology / Routing / Flow Control / Router Microarchitecture / Modeling and Evaluation / Case Studies / Conclusions / References / Authors' Biographies 是学习NOC等领域的非常好的材料。

学习神经网络的好资源，一共14个ppt，可以系统的学习神经网络的基本知识

行业分类-网络游戏-M-Learning无线网络学习系统.zip

异构网络学习排序模型及应用，杨子，唐杰，针对网络排序问题中的基础，研究了话题层次的随机游走问题，提出了3步的方法解决该任务。重点从文档集中发现话题，在计算文档话��