原文:https://www.dotnettricks.com/learn/dependencyinjection/understanding-inversion-of-control-dependency-injection-and-service-locator ----------------------------------------------------------------------------------------- Understanding Inversion of…

http://www.gamedev.net/page/resources/_/technical/game-programming/understanding-component-entity-systems-r3013 The traditional way to implement game entities was to use object-oriented programming. Each entity was an object, which intuitively allowe…

Callback functions are extremely important in Javascript. They’re pretty much everywhere. Originally coming from a more traditional C/Java background I had trouble with this (and the whole idea of asynchronous programming), but I’m starting to get th…

CSS units have been the subject of several articles here on SitePoint (such as A Look at Length Units in CSS, The New CSS3 Relative Font Sizing Units, and The Power of em Units in CSS). Today we increase the count by having an in-depth look at rem un…

https://content.pivotal.io/rabbitmq/understanding-when-to-use-rabbitmq-or-apache-kafka RabbitMQ: Erlang Apache Kafka:Scala https://content.pivotal.io/rabbitmq/understanding-when-to-use-rabbitmq-or-apache-kafka 来自谷歌翻译 了解何时使用RabbitMQ或Apache Kafka 2017年…

http://colah.github.io/posts/2015-08-Understanding-LSTMs/…

原文: https://medium.com/@coderonfleek/understanding-the-angular-boot-process-9a338b06248c --------------------------------------------------------------------------------- For newcomers to Angular 2+ (now referred to simply as Angular), the struggle i…

目录 理解 LSTM 网络 递归神经网络 长期依赖性问题 LSTM 网络 LSTM 的核心想法 逐步解析 LSTM 的流程 长短期记忆的变种 结论 鸣谢 本文翻译自 Christopher Olah 的博文 Understanding LSTM Networks,原文以图文并茂的形式,深入浅出地为初学者介绍了 LSTM 网络. [翻译]理解 LSTM 及其图示 或许可以进一步帮助理解. 理解 LSTM 网络 Understanding LSTM Networks 递归神经网络 Recurrent…

目录 理解 LSTM 及其图示 本文翻译自 Shi Yan 的博文 Understanding LSTM and its diagrams,原文阐释了作者对 Christopher Olah 博文 Understanding LSTM Networks 更加通俗的理解. Understanding LSTM Networks 中译:[翻译]理解 LSTM 网络 理解 LSTM 及其图示 我不擅长解释 LSTM,写下这段文字是为了我个人记忆方便.我认为 Christopher Olah 的那篇博文…

0x00 前言 常用的LSTM,或是双向LSTM,输出的结果通常是以下两个:1) outputs,包括所有节点的hidden2) 末节点的state,包括末节点的hidden和cell大部分任务有这些就足够了,state是随着节点间信息的传递依次变化并容纳更多信息,所以通常末状态的cell就囊括了所有信息,不需要中间每个节点的cell信息,但如果我们的研究过程中需要用到这些cell该如何是好呢? 近期的任务中,需要每个节点的前后节点cell信息来做某种判断,所以属于一个较为特殊的任务,自主实现了…