Towards Playing Full MOBA Games withDeep Reinforcement Learning.pdf

英文原版，第二版，无水印。

Learning Node.js Development_Code 源码 本资源转载自网络，如有侵权，请联系上传者或csdn删除 查看此书详细信息请在美国亚马逊官网搜索此书

Packt.Learning.Node.js.Development.1788395549 True PDF Learning Node.js Development: Learn the fundamentals of Node.js, and deploy and test Node.js applications on the web A comprehensive, easy-to-follow guide to creating complete Node apps and understanding how to build, deploy, and test your own apps. Key Features Entirely project-based and practical Explains the "Why" of Node.js features, not just the "how", providing you with a deep understanding and enabling you to easily apply concepts in your own applications Covers the full range of technologies around Node.js - NPM, version control with Git, and much more Book Description Learning Node.js Development is a practical, project-based book that provides you with all you need to get started as a Node.js developer. Node is a ubiquitous technology on the modern web, and an essential part of any web developers' toolkit. If you are looking to create real-world Node applications, or you want to switch careers or launch a side project to generate some extra income, then you're in the right place. This book has been written around a single goal-turning you into a professional Node developer capable of developing, testing, and deploying real-world production applications. Learning Node.js Development is built from the ground up around the latest version of Node.js (version 9.x.x). You'll be learning all the cutting-edge features available only in the latest software versions. This book cuts through the mass of information available around Node and delivers the essential skills that you need to become a Node developer. It takes you through creating complete apps and understanding how to build, deploy, and test your own Node apps. It maps out everything in a comprehensive, easy-to-follow package designed to get you up and running quickly. What you will learn Learn the fundamentals of Node Build apps that respond to user input Master working with servers Learn how to test and debug applications Deploy and update your apps in the real world Create responsive asynchronous web applications Who This Book Is For This book targets anyone looking to launch their own Node applications, switch careers, or freelance as a Node developer. You should have a basic understanding of JavaScript in order to follow this course. Table of Contents Chapter 1 Getting Set Up Chapter 2 Node Fundamentals - Part 1 Chapter 3 Node Fundamentals - Part 2 Chapter 4 Node Fundamentals - Part 3 Chapter 5 Basics of Asynchronous Programming in Node.js Chapter 6 Callbacks in Asynchronous Programming Chapter 7 Promises in asynchronous programming Chapter 8 Web Servers in Node Chapter 9 Deploying Applications to Web Chapter 10 Testing the Node Application part 1 Chapter 11 Testing the Node Application part 2

LearningNodeJS, 我的书附带的源代码，"Learning Node.JS" LearningNodeJS我的书附带的源代码，"学习 node.js，第二版"。 每一次，我将更新最新版本的node.js，并让每个人知道代码( 并将在源树中进行这些更改) 是否需要更改或者更新。第一版第一版源代码可以通过分支(

Using Machine Learning to Predict Student Performance.pdf，这是一份不错

Optimizing Extreme Learning Machines with Kernel Functions.zip，这

阿达·本 与论文工作相关的代码： “ AdaBnn：经过自适应结构学习训练的二值化神经网络” 该存储库当前包含两个协作笔记本： 带有实验性质的基于Keras实施AdaNet算法提出的由该文件实验“ ”在，对于学习神经网络结构为子网的集合。 此外，AdaBnn表示为对AdaNet的修改，它对运行时间施加了二进制约束，以尝试在时间方面提高性能，并且是一种基于“的正则化方式”。 “。 另外，包含的单独代码包含Adanet和AdaBnn实现及其文档。 一些发现 根据笔记本中提供的实验： 在自适应结构学习的情况下，对网络权重进行二值化具有类似的效果，即遗传算法中的突变率很高，在迭代之间很难遵循学习模式，在T迭代中不保持增量性能。 Adam优化在大多数情况下更适合于此类AdaBnn结构，并且迭代次数更少（本文中的T参数）。 目前，对AdaNet进行二值化处理并没有太大的改进，但它可能是为权重/激活添加约束作为自适应结构学习的正则化方法的起点。 进一步的工作 进一步的工作可能包括将二值化过程作为卷积子网的一部分，这是（M Courbariaux，2016）的最初建议。 例 导入依赖关

显着性方法 介绍 该存储库包含以下显着性技术的代码： XRAI *（，） SmoothGrad *（） 香草渐变（ ，） 引导反向传播（） 综合渐变（） 咬合 Grad-CAM（） 模糊IG *由PAIR开发。 此列表绝不是全面的。 我们正在接受请求添加新方法的请求！ 下载 pip install saliency 或开发版本： git clone https://github.com/pair-code/saliency cd saliency 用法 每个显着性掩码类都从SaliencyMask基类扩展。 此类包含以下方法： __init__(graph, sessio

python Deep Learning on身份件识别deepLearning_OCR-master.zip 系统共分为两部分：移动（Android）端和服务器端。移动端共分为两个模块：输入模块和输出模块；服务器端共分为三个模块：模型加载模块、模型处理模块和结果映射模块。