1.项目基于机器学习和语义识别技术，让机器人理解文本并进行合适的答复。通过使用语音与其交流，实现智能问答、智能音箱及智能机器宠物。 2.项目运行环境：包括 Python 环境、ChatterBot 环境。 Python 3.6 及以上配置。基于 chatterbot 0.8.7 开发，打开 cmd 进入 python 所在的磁盘，输入：pip install –ignore-installed –upgrade chatterbot0.8.7 等待安装即可。 3.项目包括 6 个模块：模型构建、服务器端、客户端、语音录入、接口调用、模型训练及保存。需进入百度云官网：https://ai.baidu.com/，进入我的控制台，打开百度语音进入语音应用管理界面，创建一个新的应用，并记录 APPID、API Key 和 Secret Key 三个值；聊天窗口的 GUI 界面，包括当前用户显示、信息输入框、语音输入按钮、发送和关闭按钮等各种控件并绑定发送消息、输入消息等事件。模型训练这块，可以直接使用项目中训练的模型，也可以根据自己需求替换成其他模型，如使用ChatGPT等通用大语言模型

智能算法之粒子群算法及改进. **智能算法：粒子群算法（Particle Swarm Optimization，PSO）** **资源描述：** 1. **算法原理与思想：** 粒子群算法是一种基于群体智能的优化算法，灵感来自于鸟群或鱼群等自然界中群体的协作行为。在PSO中，问题的解被看作是一群粒子，在解空间中不断移动，通过跟随历史上最优解（局部最优解）和整个群体的最优解（全局最优解）来寻找最优解。 2. **基本步骤：** - 初始化粒子群的位置和速度。 - 计算每个粒子的适应度（目标函数值）。 - 更新每个粒子的速度和位置，以便它们向着历史上最优解和群体的最优解移动。 - 重复上述步骤，直到达到预定的停止条件（例如，迭代次数达到预定值或找到满足要求的解）。 3. **算法优势：** - PSO算法简单易懂，容易实现。 - 由于其并行性，PSO适用于高维优化问题。 - PSO具有全局搜索能力，能够找到接近全局最优解的解。 4. **改进的粒子群算法：** - 多种改进的粒子群算法被提出，例如自适应权重PSO（Adapti

Machine Learning Yearning_英文版+中文版 (中文版会持续更新，并有更新的链接地址) 注：转载别人的，无商业目的，资源共享。

配合博客（https://blog.csdn.net/zhiyuan411/article/details/127854957）使用，请勿单独下载！

机器学习算法的数学解析与Python实现.docx

本文来自于简书，本文章主要通过举例来论证机器学习算法，通过矩阵进行强化学习介绍。所谓强化学习就是智能系统从环境到行为映射的学习，以使奖励信号(强化信号)函数值最大。如果Agent的某个行为策略导致环境正的奖赏(强化信号)，那么Agent以后产生这个行为策略的趋势便会加强-《百科》

已经毕业两年多了，可以帮学弟学妹们无偿帮做设计，给点奶茶费就行了哈哈。如果想自己做的话，可以看我另一篇博文，那里有我开发的一键生成设计的系统。 一键生成毕业设计 —————————————— 基于SSM的商城系统的设计与实现 基于SSM的管理系统的设计与实现 基于JSP的超市系统的设计与实现 火车订票系统的设计与实现      魔方网站的设计与实现      家庭理财管理系统设计与实现      基于卷积神经网络的图像风格化处理      基于卷积神经网络的图像修复系统设计与实现      基于深度学习的目标实例分割      基于web的云智教育在线平台设计与实现      基于纹理分析的医

电子科技大学研一《机器学习》考试重点笔记，课程内容和笔记内容参考周志华西瓜书，可以私信发ppt

电力系统负荷（电力需求量，即有功功率）预测是指充分考虑历史的系统负 荷、经济状况、气象条件和社会事件等因素的影响，对未来一段时间的系统负荷 做出预测。负荷预测是电力系统规划与调度的一项重要内容。短期（两周以内） 预测是电网内部机组启停、调度和运营计划制定的基础；中期（未来数月）预测 可为保障企业生产和社会生活用电，合理安排电网的运营与检修决策提供支持； 长期（未来数年）预测可为电网改造、扩建等计划的制定提供参考，以提高电力 系统的经济效益和社会效益。 复杂多变的气象条件和社会事件等不确定因素都会对电力系统负荷造成一 定的影响，使得传统负荷预测模型的应用存在一定的局限性。同时，随着电力系 统负荷结构的多元化，也使得模型应用的效果有所降低，因此电力系统负荷预测 问题亟待进一步研究。

Review From the reviews: "Presuming no previous background in statistics and described by the author as "demanding" yet "understandable because the material is as intuitive as possible" (p. viii), this certainly would be my choice of textbook if I was required to learn mathematical statistics again for a couple of semesters." Technometrics, August 2004 "This book should be seriously considered as a text for a theoretical statsitics course for non-majors, and perhaps even for majors...The coverage of emerging and important topics is timely and welcomed...you should have this book on your desk as a reference to nothing less than 'All of Statistics.'" Biometrics, December 2004 "Although All of Statistics is an ambitious title, this book is a concise guide, as the subtitle suggests....I recommend it to anyone who has an interest in learning something new about statistical inference. There is something here for everyone." The American Statistician, May 2005 "As the title of the book suggests, ‘All of Statistics’ covers a wide range of statistical topics. … The number of topics covered in this book is vast … . The greatest strength of this book is as a first point of reference for a wide range of statistical methods. … I would recommend this book as a useful and interesting introduction to a large number of statistical topics for non-statisticians and also as a useful reference book for practicing statisticians." (Matthew J. Langdon, Journal of Applied Statistics, Vol. 32 (1), January, 2005) "This book was written specifically to give students a quick but sound understanding of modern statistics, and its coverage is very wide. … The book is extremely well done … ." (N. R. Draper, Short Book Reviews, Vol. 24 (2), 2004) "This is most definitely a book about mathematical statistics. It is full of theorems and proofs … . Presuming no previous background in statistics … this certainly would be my choice of textbook if I was required to learn mathematical statistics again for a couple of semesters." (Eric R. Ziegel, Technometrics, Vol. 46 (3), August, 2004) "The author points out that this book is for those who wish to learn probability and statistics quickly … . this book will serve as a guideline for instructors as to what should constitute a basic education in modern statistics. It introduces many modern topics … . Adequate references are provided at the end of each chapter which the instructor will be able to use profitably … ." (Arup Bose, Sankhya, Vol. 66 (3), 2004) "The amount of material that is covered in this book is impressive. … the explanations are generally clear and the wide range of techniques that are discussed makes it possible to include a diverse set of examples … . The worked examples are complemented with numerous theoretical and practical exercises … . is a very useful overview of many areas of modern statistics and as such will be very useful to readers who require such a survey. Library copies would also see plenty of use." (Stuart Barber, Journal of the Royal Statistical Society, Series A – Statistics in Society, Vol. 168 (1), 2005) Product Description This book is for people who want to learn probability and statistics quickly. It brings together many of the main ideas in modern statistics in one place. The book is suitable for students and researchers in statistics, computer science, data mining and machine learning. This book covers a much wider range of topics than a typical introductory text on mathematical statistics. It includes modern topics like nonparametric curve estimation, bootstrapping and classification, topics that are usually relegated to follow-up courses. The reader is assumed to know calculus and a little linear algebra. No previous knowledge of probability and statistics is required. The text can be used at the advanced undergraduate and graduate level. Larry Wasserman is Professor of Statistics at Carnegie Mellon University. He is also a member of the Center for Automated Learning and Discovery in the School of Computer Science. His research areas include nonparametric inference, asymptotic theory, causality, and applications to astrophysics, bioinformatics, and genetics. He is the 1999 winner of the Committee of Presidents of Statistical Societies Presidents' Award and the 2002 winner of the Centre de recherches mathematiques de Montreal-Statistical Society of Canada Prize in Statistics. He is Associate Editor of The Journal of the American Statistical Association and The Annals of Statistics. He is a fellow of the American Statistical Association and of the Institute of Mathematical Statistics.