前段时间和头条的一个工程师聊天，他在发布校招信息后发现投NLP算法的非常多，但是投推荐系统的寥寥，一方面，可能是我这边同学样本偏NLP的居多，另外一个很重要的原因，在校的同学很难有机会接触到大厂的推荐系统。且不说在校的同学，就是中小厂的工程师，也很难有机会接触稍大一些规模的推荐系统。一方面，大厂有需求有动力去搞推荐系统和推荐算法，这是可以转化为真金白银的，另外一方面，大的需求背后又是相对匮乏的推荐系统体系化知识普及：在复杂网络中，雄踞顶端的节点无法体会长尾的疾苦。推荐系统的技术应用现状也如此，大厂门一骑绝尘，感觉分分钟就要达到奇点的节奏，然而更普遍的是：太多中小厂、工程师们还不知道一个推荐系统如何才能从0到1诞生，这需要去了解哪些知识？这是《推荐系统三十六式》开篇词”用知识去对抗技术不平等”的一段话，这段话以及这篇文章深得我心，所以今天就安利一下这个专栏，先介绍一下有丰富业界推荐系统实战经验的刑无刀老师：刑无刀，本名陈开江，现在是”贝壳找房“资深算法专家，从事算法类产品的研发。曾任新浪微博资深算法工程师，考拉FM算法主管。从业8年时间，他的工作和研究范围始终没有超出推荐系统，同时也是中国最专业推荐技术与产品社区之一ResysChina的特约作者，不仅经验丰富，还很会写。这些年，他曾服务过创业公司、传统大公司和大型互联网公司，这见证了大大小小、形状各异的推荐系 ...继续阅读 (19)

Elasticsearch视频新课，感兴趣的同学可以看看：Elasticsearch核心技术与实战搜索是现代软件必备的一项基础功能，而 Elasticsearch 就是一款功能强大的开源分布式搜索与数据分析引擎。它可以从海量数据中快速找到相关信息，在同领域内几乎没有竞争对手——近两年 DBRanking 的数据库评测中，ES 在搜索引擎领域始终位列第一。当你在 GitHub 上搜索时，Elasticsearch 可以实现代码级的搜索与高亮显示 ；当你在网上购物时，ES 可为你推荐喜欢的商品；当你下班打车回家时，ES 可以通过定位附近的乘客和司机，帮助平台优化调度。Elasticsearch 还被广泛运用于大数据近实时分析，包括日志分析、指标监控、信息安全等多个领域。它可以探索海量结构化、非结构化数据，按需创建可视化报表，对监控数据设置报警阈值，甚至通过使用机器学习，做到自动识别异常状况。作为目前最流行的开源搜索引擎，ES 在全球的下载量已超过 3.5 亿次，腾讯、滴滴、今日头条、饿了么、360 安全、小米，vivo 等国内诸多知名公司都在使用 Elasticsearch。Elasticsearch 有什么特点？Elasticsearch 非常容易上手，具有开箱即用的特性，你可以在极短的时间内设置好开发环境，然后快速上手使用，继而在成百上千台服务器上实现 PB 级的数据处理。虽然上手 ...继续阅读 (28)

毋庸置疑，目前 Apache Kafka 是整个消息引擎领域的执牛耳者，也是大数据生态圈中颇为重量级的一员。从最早诞生于 LinkedIn 的“分布式消息系统”，到现在集成了分发、存储和计算的“流式数据平台”，Kafka 广泛应用于国内外大厂，比如 BAT、字节跳动、美团、Netflix、Airbnb、Twitter 等等。我身边也有越来越多的工程师们，把 Kafka 加入到自己的学习列表。的确，我们仅需要学习一套框架，就能在实际业务系统中实现消息队列应用、应用程序集成、分布式存储构建，甚至是流处理应用的开发与部署，可谓相当超值了。不过，想要学透 Kafka 没有想象中的那么简单，学习路径和方法尤为重要。我的建议是：千万不要直接扎到具体的细节中，亦或是从一个很小的点开始学习。因为你无法建立全局的认知观，从而实现系统地学习。在我看来，针对不同的角色，Kafka 的学习路径是不一样的。如果你是软件开发工程师，可以先根据编程语言寻找对应的 Kafka 客户端，然后去官网上学习代码示例，正确编译和运行这些样例。接下来，你可以尝试修改样例代码并使用其他的 API，之后观测你修改的结果。如果这些都没有难倒你，你可以自己编写一个小型项目来验证下学习成果，然后就是改善和提升客户端的可靠性和性能了。到了这一步，就熟读一遍 Kafka 文档吧，确保你理解了那些可能影响可靠性和性能的参数。最后是学习 K ...继续阅读 (46)

线性代数的相关课程里，我最推崇的还是MIT Gilbert Strang教授的线性代数课程，关于这方面资源的介绍，可以参考我们之前那篇文章：那些值得推荐和收藏的线性代数学习资源最近发现他依然活跃在教学一线，在MIT 2018春季学期开设了一门的相关课程：Matrix Methods in Data Analysis, Signal Processing, and Machine Learning这门课程可以翻译为“数据分析、信号处理和机器学习中的矩阵方法”，课程主页的配图特别能说明问题：以下是该课程资源链接，感兴趣的同学可以参考：课程主页：https://ocw.mit.edu/courses/mathematics/18-065-matrix-methods-in-data-analysis-signal-processing-and-machine-learning-spring-2018/课程官方视频：https://www.youtube.com/playlist?list=PLUl4u3cNGP63oMNUHXqIUcrkS2PivhN3k爱可可老师B站搬运链接：https://www.bilibili.com/video/av53055190/ ...继续阅读 (25)

推荐一门有意思的课程：OpenResty 从入门到实战对服务端工程师来说，如果你的工作中涉及 Nginx、高性能、高并发、动态控制、性能测试、分析等，那么，不论你使用什么开发语言和平台，OpenResty 都是不可多得的高性能开发利器。但我发现，有不少人没接触过 OpenResty，是时候打开服务端新世界的大门了。作为一个兼具开发效率和性能的服务端开发平台，OpenResty 并不像其他开发语言一样从零开始搭建，而是基于成熟的开源组件—— Nginx 和 LuaJIT。其核心是基于 Nginx 的一个 C 模块（lua-nginx-module），这个模块将 LuaJIT 嵌入到 Nginx 服务器中，并对外提供一套完整的 Lua API，透明地支持非阻塞 I/O，提供了轻量级线程、定时器等高级抽象。围绕这个模块，OpenResty 构建了一套完备的测试框架、调试技术和由 Lua 实现的周边功能库。你可以用 Lua 进行字符串和数值运算、查询数据库、发送 HTTP 请求、执行定时任务、调用外部命令等，还可以用 FFI 的方式调用外部 C 函数。这基本上满足了服务端开发所需要的全部功能，所以，OpenResty 的适用范围早已超出了反向代理和负载均衡。这就意味着，掌握了 OpenResty，就能同时拥有脚本语言的开发效率和迭代速度，以及 Nginx C 模块的高并发和高性能优势。通过 ...继续阅读 (25)

极客时间新课零基础学 Java：PayPal 技术专家手把手带你入门 Java，快速掌握 Java 编程技巧http://gk.link/a/103ol作为最受欢迎的编程语言之一，Java 已经走过了20多个年头。从已经落寞的诺基亚到现在火热的电商系统，我们都能看到 Java 语言的身影。虽然这几十年来，也都陆续有众多其它语言出现，但它们在普及程度上仍然无法与 Java 相提并论。所以，我也认为，在未来十年，Java 仍将会是最受欢迎的编程语言。如果你想成为一名合格的后端工程师，并且想入职知名的互联网公司，那我推荐你深入学习 Java。原因如下：薪水高。Java 工程师的月薪普遍在 10k-50k 之间，相比于一般职位，可谓“高薪”。工作岗位较多。包括阿里巴巴、京东、去哪儿网、亚马逊在内的知名公司都在使用 Java 语言。生态非常成熟。Java 不仅仅只是一种主流编程语言，它同时也代表着一整个活跃的生态系统。你在大数据、微服务、云计算等热门领域都能看到它的身影。可以说，学好了 Java，不愁没有用武之地，未来的开发之路会非常宽广。对于初学者而言，一开始很容易被各种编程语言的抽象概念吓倒。比如常量、字符类、对象、封装、继承、等等，虽然字都认识，但就是搞不明白什么意思，有时候如果过于关注某个概念的字面含义，反而更容易跑偏。另外，好不容易把这些抽象的概念都弄懂了，但却不知道该怎样把它们串 ...继续阅读 (22)

推荐FB资深工程师景霄开设的《Python核心技术与实战》新专栏，从工程的角度，重新理解 Python，做到真正的“即学即用”http://gk.link/a/103fc作者介绍：景宵本科毕业于武汉大学电子工程系（专业第一），后进入哥伦比亚大学攻读计算机硕士学位。毕业后顺利入职Facebook，目前在核心组Feed Ranking，从事机器学习的相关工作。这个专栏是从工程的角度来讲解Python这门编程语言，不会死抠一些很偏的知识点，相反，会从实际出发，以工作中遇到的实例为主线，去讲解Python的知识点和应用。本专栏中基于Python最新版本3.7，有大量独特的案例和见解，以及不少作者本人阅读源码后的体会，并且难易兼顾，专栏中既有简单的基础知识，也有进阶的课程，会尽量的做到“老少皆宜”。你将获得：从工程角度掌握 Python 高阶用法独立开发 Python 项目的能力完整的 Python 学习路径硅谷一线工程师的独家经验分享新目录一、Python基础知识方法：如何逐步突破，成为Python高手（Jingxaio）工具：为什么必学Jupyter Notebook ？ （Yuannan）列表和元祖，到底用哪一个？（Jingxaio）字典、集合，你真的了解吗？ （Jingxiao）深入浅出字符串 （Jingxiao）Python“黑箱”：输入与输出 (Tan)修炼基本功：条件与循环 ...继续阅读 (28)

李航老师《统计学习方法（第二版）》出版了，以下是相关介绍。内 容 简 介统计学习方法即机器学习方法，是计算机及其应用领域的一门重要学科。本书分为监督学 习和无监督学习两篇，全面系统地介绍了统计学习的主要方法。包括感知机、k 近邻法、朴素贝 叶斯法、决策树、逻辑斯谛回归与最大熵模型、支持向量机、提升方法、EM 算法、隐马尔可夫 模型和条件随机场，以及聚类方法、奇异值分解、主成分分析、潜在语义分析、概率潜在语义分 析、马尔可夫链蒙特卡罗法、潜在狄利克雷分配和 PageRank 算法等。除有关统计学习、监督学 习和无监督学习的概论和总结的四章外，每章介绍一种方法。叙述力求从具体问题或实例入手， 由浅入深，阐明思路，给出必要的数学推导，便于读者掌握统计学习方法的实质，学会运用。为满足读者进一步学习的需要，书中还介绍了一些相关研究，给出了少量习题，列出了主要参 考文献。 本书是统计机器学习及相关课程的教学参考书，适用于高等院校文本数据挖掘、信息检索及自然语言处理等专业的大学生、研究生，也可供从事计算机应用相关专业的研发人员 参考。第二版序言《统计学习方法》第一版于2012年出版，讲述了统计机器学习方法，主要是一些常用的监督学习方法．第二版增加了一些常用的无监督学习方法，由此本书涵盖了传统统计机器学习方法的主要内容．在撰写《统计学习方法》伊始，对全书内容做了初步规划．第一版出版之后，即着手无监 ...继续阅读 (21)

极客时间新课《Web 协议详解与抓包实战》视频课，访问课程主页：http://gk.link/a/103bR发现大部分技术人，多多少少都会遇到下面的困惑，比如：AJAX 跨域访问时总被浏览器的同源策略阻止，其背后的原理及符合其设计思想的解决方案是什么？缓存可以提升性能，但什么样的 HTTP 消息才会被浏览器或者代理服务器缓存呢？抓包时收到 4xx 或者 5xx HTTP 响应码，这意味着服务器端发生了什么？当我们维护 HTTPS 连接时，如果要优化TLS/SSL 的卸载性能，应该从哪里入手？定位问题时，发现本地的 TCP 连接状态中出现了FIN_WAIT、CLOSE_WAIT 或者TIME_WAIT，都是什么原因引起的？应该如何解决？想要提升 TCP 连接的传输速度，可服务器上相关的系统参数多达数十个，究竟该怎么调整？时下逐渐普及的 IPv6 ，在报文上到底与 IPv4 协议有何差别？你会发现，这些问题其实都和 Web 协议密切相关。我常常听到身边人抱怨“学不动了”，之所以会这样，大多是因为没有掌握好互联网体系中的底层知识。这些知识点相对稳定，而其他时刻变化的知识点是构建在这些知识点之上的。一旦掌握好，就可以长久使用，并且保持快速学习的能力。Web 协议就是其中非常重要的一个模块。虽然它常常要兼容 internet 中非常古老或者有着突破性创新的浏览器或者服务器，从而也在时刻进化， ...继续阅读 (19)

极客时间刚刚上线了一门新课:深入浅出计算机组成原理，作者徐文浩老师上来就告诉大家他是做自然语言处理创业的，很有意思，其实我心理想，敢开这些CS基础课的，都是牛人，能学这些课程的同学，都在夯实地基，我就想看看他是如何深入浅出讲这门课的。对于程序员来说，计算机组成原理相关知识重要吗？不知道大家有没有发现各个大学计算机系的培养计划，它们都有差不多十来门核心课程。其中，“计算机组成原理”却是入门和底层知识的第一课。为什么这样说？想象一下，一名赛车手不知道赛车的内部构成和工作原理，你觉得他能驾驭赛车，在专业赛事中夺冠吗？同样的道理，要成为一名优秀的软件工程师，写得一手好程序，就要了解计算机是怎么工作的，深入计算机组成原理。我们直接用肉眼来看，计算机是由CPU、内存、显示器这些设备组成的硬件，而大部分人工作后从事的是软件开发，显然，在硬件和软件之间需要一座桥梁，而“计算机组成原理”就扮演了这样一个角色，它既隔离了软件和硬件，也提供了让软件无需关心硬件，就能直接操作硬件的接口。简单地说，你只需要对硬件有原理性的理解，就可以信赖硬件的可靠性，安安心心用高级语言来写程序。无论是写操作系统、编译器这样的硬核代码，还是写Web应用、手机App这样的应用层代码，你都可以做到心里有底。如上图，学习组成原理之后，向下，你可以学习数字电路相关的课程，向上，你可以学习编译原理、操作系统这些核心课程。它会让你真正对 ...继续阅读 (24)

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