蠕虫链

顺序拟合 动机 如果我们有一个只能采样的未知函数f(x) ，我们可以选择一个以参数向量p特征的已知函数g(x,p) 。 用最小二乘法，我们可以找到p最小化的总和-的平方误差\sum_{x\in X}(g(x,p) - f(x))^2以设定的采样点的X 。 如果评估f昂贵，那么仔细选择采样点符合我们的利益。 假设我们的模型已经很不错了，我们可以使用它来找出下一步要采样的地方。 猜测要采样的点是x^* ，其中g(x^*,p)的p梯度尽可能大（这是我们最有可能从采样中学到的东西）的地方。 我们还希望避免在同一位置多次采样。 该程序包实现了这种顺序采样方法。 使用范例 using SequentialFit, Plots gaussian (x,mu,sigma) = exp ( - ((x - mu) / sigma) ^ 2 ) function expensiveFunction (x

Julia编程语言 优点 免费开源 核心语言非常小。标准库是用Julia语言本身写的 如线性代数，随机数生成，快速傅里叶变换，字符串处理…… 丰富的用于建立或描述对象的类型语法 高端，接近于静态编译型语言。包括用户自定义类型等 为并行计算和分布式计算而设计 轻量级“绿色”协程 优雅的可扩展的类型转换/提升 支持Unicode，包括但不限于UTF-8 可直接调用C函数（不需要包装或采用特殊的API） 有类似的壳的进展管理能力 有类似Lisp的宏以及其他元编程工具

GeneticAlgorithms.jl, 在Julia中，编写遗传算法的轻量级框架 GeneticAlgorithms.jl 这个轻量级框架简化了创建遗传算法并同时运行它们的过程。基本用法你有什么问题？假设你有一个简单的等式 a 2b 3c 4d 5e = 42 你想找到一个解决方法。创建模

ML算法： 线性回归 逻辑回归 朴素贝叶斯 知识网络 K均值 决策树 随机森林 支持向量机 ANN ....等 排序算法： 快速排序 气泡排序 合并排序 插入排序...等 数据结构和算法： Queue列 二叉树 链表 跳过清单 BFS，DFS ....等 DSP和多媒体技术： 快速傅立叶变换 小波 哈夫曼...等

使用Julia语言进行数据科学方面的工作，数据分析，数据可视化

EcoBase.jl

CompatHelper.jl：自动更新Julia程序包的依存关系的[compat]条目

计算机责成与体系结构课后大全 Linda Null编的书 答案很详细很准确，是学习英文版书的很好的辅助资源

Hands-On Computer Vision with Julia: Build complex applications with advanced Julia packages for image processing, neural networks, and Artificial Intelligence Explore the various packages in Julia that support image processing and build neural networks for video processing and object tracking. Key Features Build a full-fledged image processing application using JuliaImages Perform basic to advanced image and video stream processing with Julia's APIs Understand and optimize various features of OpenCV with easy examples Book Description Hands-On Computer Vision with Julia is a thorough guide for developers who want to get started with building computer vision applications using Julia. Julia is well suited to image processing because it's easy to use and lets you write easy-to-compile and efficient machine code. This book begins by introducing you to Julia's image processing libraries such as Images.jl and ImageCore.jl. You'll get to grips with analyzing and transforming images using JuliaImages; some of the techniques discussed include enhancing and adjusting images. As you make your way through the chapters, you'll learn how to classify images, cluster them, and apply neural networks to solve computer vision problems. In the concluding chapters, you will explore OpenCV applications to perform real-time computer vision analysis, for example, face detection and object tracking. You will also understand Julia's interaction with Tesseract to perform optical character recognition and build an application that brings together all the techniques we introduced previously to consolidate the concepts learned. By end of the book, you will have understood how to utilize various Julia packages and a few open source libraries such as Tesseract and OpenCV to solve computer vision problems with ease. What you will learn Analyze image metadata and identify critical data using JuliaImages Apply filters and improve image quality and color schemes Extract 2D features for image com