黑曜石心灵地图 该存储库包含一个用于的插件，可使用将Markdown注释作为思维导图。 提供了类似的插件。 特征 将您当前的笔记预览为思维导图 当您选择其他窗格时，思维导图预览会更新，类似于“ ，“和“窗格 用法 您可以使用命令打开当前注释的思维导图预览。 预览更多选项菜单 思维导图预览视图具有“更多选项”菜单中的2个选项： 别针 允许您将“思维导图”预览窗格固定到当前注释，以便您可以选择其他注释，并保留当前的思维导图。 图钉图标将出现在“思维导图”预览窗格的标题中。 点击固定图标以取消固定。 复制屏幕截图 将“思维导图” SVG副本放置在剪贴板上，使您可以将其粘贴到“黑曜石”中的注释或您选择的图像编辑器中。 兼容性 自定义插件仅适用于黑曜石v0.9.7 +。 此仓库的当前API以Obsidian v0.9.20为目标。 正在安装 从Obsidian的0.9.7版本开始，可

Written in an easily accessible style, this book provides the ideal blend of theory and practical, applicable knowledge. It covers neural networks, graphical models, reinforcement learning, evolutionary algorithms, dimensionality reduction methods, and the important area of optimization. It treads the fine line between adequate academic rigor and overwhelming students with equations and mathematical concepts. The author includes examples based on widely available datasets and practical and theoretical problems to test understanding and application of the material. The book describes algorithms with code examples backed up by a website that provides working implementations in Python.

数据挖掘与文本分析，类似于英文杂志的阅读科普材料，针对UK的语言机器发展史

A First Course in Machine Learning, Second Edition (Machine Learning & Pattern Recognition) by Simon Rogers, Mark Girolami 2016 | ISBN: 1498738486 | English | 427 pages | PDF | 161 MB "A First Course in Machine Learning by Simon Rogers and Mark Girolami is the best introductory book for ML currently available. It combines rigor and precision with accessibility, starts from a detailed explanation of the basic foundations of Bayesian analysis in the simplest of settings, and goes all the way to the frontiers of the subject such as infinite mixture models, GPs, and MCMC." ―Devdatt Dubhashi, Professor, Department of Computer Science and Engineering, Chalmers University, Sweden "This textbook manages to be easier to read than other comparable books in the subject while retaining all the rigorous treatment needed. The new chapters put it at the forefront of the field by covering topics that have become mainstream in machine learning over the last decade." ―Daniel Barbara, George Mason University, Fairfax, Virginia, USA "The new edition of A First Course in Machine Learning by Rogers and Girolami is an excellent introduction to the use of statistical methods in machine learning. The book introduces concepts such as mathematical modeling, inference, and prediction, providing ‘just in time’ the essential background on linear algebra, calculus, and probability theory that the reader needs to understand these concepts." ―Daniel Ortiz-Arroyo, Associate Professor, Aalborg University Esbjerg, Denmark "I was impressed by how closely the material aligns with the needs of an introductory course on machine learning, which is its greatest strength…Overall, this is a pragmatic and helpful book, which is well-aligned to the needs of an introductory course and one that I will be looking at for my own students in coming months." ―David Clifton, University of Oxford, UK "The first edition of this book was already an excellent introductory text on machine learning for an advanced undergraduate or taught masters level course, or indeed for anybody who wants to learn about an interesting and important field of computer science. The additional chapters of advanced material on Gaussian process, MCMC and mixture modeling provide an ideal basis for practical projects, without disturbing the very clear and readable exposition of the basics contained in the first part of the book." ―Gavin Cawley, Senior Lecturer, School of Computing Sciences, University of East Anglia, UK "This book could be used for junior/senior undergraduate students or first-year graduate students, as well as individuals who want to explore the field of machine learning…The book introduces not only the concepts but the underlying ideas on algorithm implementation from a critical thinking perspective." ―Guangzhi Qu, Oakland University, Rochester, Michigan, USA

A First Course in Machine Learning MATLAB 2009

Mind fusion chart最新破解代码 拿走不谢，自带工程，可以编译成功！

星际争霸AI 希望路过的同学，顺手给JStarCraft框架点个Star，算是对作者的一种鼓励吧！ JStarCraft AI是一个机器学习的轻量级框架。遵循Apache 2.0协议。 在学术界，大规模研究人员使用的编程语言是Python。 在工业界，大规模开发人员使用的编程语言是Java。 JStarCraft AI是一个基于Java语言的机器学习工具包，由一系列的数据结构，算法和模型组成。 目标是作为在学术界与工业界的机器机器研究研发的相关人员之间的主轴。 作者 洪钊桦 电子邮件 ， JStarCraft AI架构 JStarCraft AI框架各个模块之间的关系： JStarCraft AI特性 属性与特征 连续 离散 模块与实例 选择，排序与切割 2.环境（environment） 串行计算 并行计算 CPU计算 GPU计算 3.数学（数学） 算法（算法） 微积分（微积分） 相关性（correlation） 距离（distance） 相似度 分解（分解） 核技巧（内核） 概率 标量 方法 矩阵 张量 单元 表单 4.调制标准（调制解调器） 线性模型（linear） 近邻

Zomato餐厅数据分析和推荐系统 :fire: 语境 班加罗尔的饮食文化让我着迷。 班加罗尔（Bengaluru）遍布世界各地的餐厅。 从美国到日本，从俄罗斯到南极洲，您可以在这里找到所有类型的美食。 送货，外出就餐，酒吧，酒吧，饮料，自助餐，甜品，您自己定的名字，班加罗尔也有。 班加罗尔是美食家的最佳去处。 餐厅的数量每天都在增加。 目前拥有约12,000家餐厅。 拥有如此众多的餐厅。 这个行业还没有饱和。 新餐厅每天都在营业。 然而，与已建立的餐馆竞争已经变得困难。 继续对他们构成挑战的关键问题包括高昂的房地产成本，不断上涨的食品成本，缺乏优质的人力，分散的供应链和过度的许可。 该Zomato数据

餐馆评论分析 使用自然语言处理和单词袋进行特征提取，以分析在餐厅用餐的顾客的情绪，最后使用分类算法将正面和负面情绪分开。 餐馆评论分析使用自然语言处理和词袋进行特征提取，以对在餐馆中拜访的顾客进行情感分析，最后使用分类算法将正面和负面情绪分开。 自然语言处理自然语言处理是计算机科学，信息工程和人工智能的一个子领域，与计算机和人类语言之间的交互有关，尤其是如何对计算机进行编程以处理和分析大量自然语言数据 使用机器学习算法对分类进行分类，以分离不同的情感，以更好地了解商业环境

Report Machine 3.0 f（for delphi XE5- XE10）RM 报表打印组件 支持XE10 解压后运行 ProjectGroup_xe10.groupproj 然后 安装 rm_d_xe7 编译 rm_r_xe7 设置lib到 source 目录