Redis学习资料

本文介绍了作者初次接触Redis的体验,以及Redis的环境安装和简单测试。文章提供了Redis的W3C教程链接,并分享了从入门到进阶的Redis学习笔记,涵盖数据类型、命令、键值设计、优化机制、事务、管道、发布/订阅、持久化、主从复制和分布式等内容,适合Java开发者使用Jedis进行操作。

今天刚刚接触Redis,在win7下装了环境,测试了一下,感觉挺不错的,命令符合编程习惯.它支持的语言也挺多的,对于Java开发,使用Jedis即可操作Redis.过段时间把它加入到我的阿里云上,试试效果....大笑


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Redis 教程-W3C

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Redis学习笔记1--入门篇 

Redis学习笔记2--Redis数据类型及相关命令

Redis学习笔记3--Redis键值设计

Redis学习笔记4--Redis数据存储优化机制

Redis学习笔记5--Redis排序

Redis学习笔记6--Redis事务

Redis学习笔记7--Redis管道(pipeline)

Redis学习笔记8--Redis发布/订阅

Redis学习笔记9--Redis持久化

Redis学习笔记10--Redis主从复制

Redis学习笔记11--Redis分布式

About This Book Model and design databases effectively in Redis Learn about sharding, indexing, and migrating strategies in Redis A simple step-by-step tutorial explaining the main concepts of Redis through practical examples Who This Book Is For This book is for SQL developers who want to learn about Redis, the key value database for scalability and performance. Prior understanding of a programming language is essential; however no knowledge of NoSQL is required. What You Will Learn Familiarise yourself with NoSQL and install Redis Build solutions and enhance your web applications in Redis Understand the persistent mechanism for better scalability Configure and tune the server to improve performance Identify bottlenecks and how to handle fault management in Redis Learn about backups and recovery strategies for the Redis environment Discover the commands and functions of Redis In Detail This book will introduce Redis and help you understand its various facets. Starting with an introduction to NoSQL, you will learn how to install Redis and how to classify and work with data structures. By working with real world scenarios pertaining to using Redis, you will discover sharding and indexing techniques, along with how to improve scalability and performance through persistent strategies and data migration techniques. With the help of multiple examples, you will learn to design web and business applications. You will also learn how to configure Redis for setting up clusters and tuning it for performance. At the end of this book, you will find essential tips on backup and recovery strategies for the Redis environment.
内容概要:本文介绍了基于贝叶斯优化的CNN-LSTM混合神经网络在时间序列预测中的应用,并提供了完整的Matlab代码实现。该模型结合了卷积神经网络(CNN)在特征提取方面的优势与长短期记忆网络(LSTM)在处理时序依赖问题上的强大能力,形成一种高效的混合预测架构。通过贝叶斯优化算法自动调参,提升了模型的预测精度与泛化能力,适用于风电、光伏、负荷、交通流等多种复杂非线性系统的预测任务。文中还展示了模型训练流程、参数优化机制及实际预测效果分析,突出其在科研与工程应用中的实用性。; 适合人群:具备一定机器学习基基于贝叶斯优化CNN-LSTM混合神经网络预测(Matlab代码实现)础和Matlab编程经验的高校研究生、科研人员及从事预测建模的工程技术人员,尤其适合关注深度学习与智能优化算法结合应用的研究者。; 使用场景及目标:①解决各类时间序列预测问题,如能源出力预测、电力负荷预测、环境数据预测等;②学习如何将CNN-LSTM模型与贝叶斯优化相结合,提升模型性能;③掌握Matlab环境下深度学习模型搭建与超参数自动优化的技术路线。; 阅读建议:建议读者结合提供的Matlab代码进行实践操作,重点关注贝叶斯优化模块与混合神经网络结构的设计逻辑,通过调整数据集和参数加深对模型工作机制的理解,同时可将其框架迁移至其他预测场景中验证效果。
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