嗨,我是RyuzakiBot! 寻找免费的开源聊天机器人? RyuzakiBot是一个简单的基于检索的聊天机器人,使用NLTK和scikit-learn在Python3中从头开始制作。 在此处尝试: : 请注意,该网站已部署在免费的Heroku服务器上,并且首次加载和响应需要一些时间。 使用自己的语料库 如果您想对RyuzakiBot进行其他主题的培训,请自行更改corpus.txt文件。 创建一个不难,每个语料库只是各种输入语句及其对聊天机器人的响应进行训练的样本。 在上面的示例中,它将使用的Wikipedia页面作为语料库。 API REST RyuzakiBot使用了微框架及其扩展,增加了对快速构建REST API的支持: 。 您可以在此处向API发出HTTPS请求: : q=将保留查询,并且所有GET请求都将返回JSON对象。 实作 这个聊天机器人是用Python3编写的,主要使用: NLTK:是自然语言处理(NLP)和人工智能库。 NLTK用于文本预处理(消除噪声,停用词,词干和词形去除)。 请访问了解更多信息。 scikit-learn:是一个数据挖掘和数据
2021-11-21 12:04:25 15KB scikit-learn chatbot python3 nltk
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IE598金融学机器学习,2018秋季,伊利诺伊大学香槟分校 马克最终小组项目 作者:约瑟夫·洛斯(Joseph Loss),杨若中,徐凤凯,冯彪和段玉辰 型号概要: 探索性数据分析 预处理,特征提取,特征选择 模型拟合和评估,(您至少应拟合3种不同的机器学习模型) 超参数调整 组装 IE598金融学机器学习,2018年秋季最终小组项目 作者:约瑟夫·洛斯(Joseph Loss),杨若中,徐凤凯,冯彪和段玉辰
2021-11-21 09:50:16 6.12MB python data-science machine-learning scikit-learn
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Learn Qt 5 Build modern, responsive cross-platform desktop applications with Qt, C++, and QML 英文mobi 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
2021-11-19 20:25:32 4.01MB Learn Build modern responsive
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sklearn.docset Zeal/Dash 的 Scikit-learn 文档集。 安装 将 sklearn.docset.tgz 文件解压到~/.local/share/Zeal/docsets/ 。
2021-11-19 15:48:45 2KB Shell
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Committee on Developments in the Science of Learning with additional material from the Committee on Learning Research and Educational Practice,National Research Council ISBN: 0-309-50145-8, 385 pages, 7 x 10, (2000)
2021-11-19 14:56:15 4.83MB learn
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使用神经网络预测时间序列 :blue_circle: 联系人:对于Bitcoin的学习材料组织列表,请点击此 ,这等 介绍 不管比特币价格上的投机泡沫如何,该项目的目的都是暗示该加密货币的未来收盘价。 根据我的分析考虑了几个比特币指标,收集了情绪数据以及区块链,历史价格和金融指数数据以预测收盘价。 环境设定 要运行预测模型,应安装以下内容: Python 3+ Tensorflow = 1.10.1 Keras = 2.2.2 熊猫= 0.23.4 脾气暴躁= 1.15.1 Matplotlib = 2.2.3 sklearn = 0.19.2 ML实现的神经网络 LSTM
2021-11-19 14:54:15 134.55MB time-series tensorflow numpy scikit-learn
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使用机器学习识别欺诈(项目概述) 项目目标 在2000年,安然(Enron)是美国最大的公司之一。 到2002年,由于广泛的公司欺诈行为,该公司破产了。 在最终的联邦调查中,大量的通常是机密信息被输入到公共记录中,包括成千上万的电子邮件和高级管理人员的详细财务数据。 这些数据已与手工生成的欺诈案件中感兴趣的人的名单相结合,这意味着被起诉,与政府达成和解或辩诉交易或作证以换取起诉豁免权的个人。 这些数据为146名员工创建了21个要素的数据集。 该项目的范围是创建一种算法,该算法能够识别可能实施欺诈的安然员工。 为了实现此目标,部署了探索性数据分析和机器学习以从异常值中清除数据集,识别新参数并将
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Learn Swift by Building Applications: Explore Swift programming through iOS app development by Emil Atanasov Packt Publishing English 2018-05-25 366 pages 5.0/5.0 1 reviews Details Title: Learn Swift by Building Applications: Explore Swift programming through iOS app development Author: Emil Atanasov Length: 366 pages Edition: 1 Language: English Publisher: Packt Publishing Publication Date: 2018-05-25 ISBN-10: 178646392X ISBN-13: 9781786463920 Sales Rank: #1123253 (See Top 100 Books) Categories Computers & Technology Mobile Phones, Tablets & E-Readers Operating Systems Programming Programming Languages Description Start building your very own mobile apps with this comprehensive introduction to Swift and object-oriented programming Key Features A complete beginner's guide to Swift programming language Understand core Swift programming concepts and techniques for creating popular iOS apps Start your journey toward building mobile app development with this practical guide Book Description Swift Language is now more powerful than ever; it has introduced new ways to solve old problems and has gone on to become one of the fastest growing popular languages. It is now a de-facto choice for iOS developers and it powers most of the newly released and popular apps. This practical guide will help you to begin your journey with Swift programming through learning how to build iOS apps. You will learn all about basic variables, if clauses, functions, loops, and other core concepts; then structures, classes, and inheritance will be discussed. Next, you'll dive into developing a weather app that consumes data from the internet and presents information to the user. The final project is more complex, involving creating an Instagram like app that integrates different external libraries. The app also uses CocoaPods as its package dependency manager, to give you a cutting-edge tool to add to your skillset. By the end of the book, you will have learned how to model real-world apps
2021-11-18 23:05:46 16.8MB IOS SWIFT ios12 Emil
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Deep Learn Toolbox 深度学习的matlab工具箱
2021-11-18 21:46:44 14.09MB 深度学习
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kdd99-scikit scikit-learn使用决策树(CART)和多层感知器解决kdd99数据集的解决方案 Kdd99数据集简介 是建立一个网络入侵检测器,这是一种能够区分“不良”连接(称为入侵或攻击)和“良好”正常连接的预测模型。 请注意,测试数据并非与训练数据具有相同的概率分布,并且包括不在训练数据中的特定攻击类型。 训练数据快照( raw/kddcup.data_10_percent.txt ): 0,tcp,http,SF,181,5450,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,8,8,0.00,0.00,0.00,0.00,1.00,0.00,0.00,9,9,1.00,0.00,0.11,0.00,0.00,0.00,0.00,0.00,normal. 0,tcp,http,SF,239,486,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,8,8,0.00,0.00,0.00,0.00,1.00,0.00,0.00,19,19,1.00,0.00,0.05,0.00,0.00,0.00,0.00,0.00,norm
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