Gradient Clipping and Its Role
This article briefly introduces the gradient clipping method and its role. I recently found this mechanism has a huge impact on results when training RNNs.
梯度裁剪及其作用
本文简单介绍梯度裁剪 (gradient clipping) 的方法及其作用，最近在训练 RNN 过程中发现这个机制对结果影响非常大。
Implementing LRCN Model with Keras
This article mainly introduces how to implement the LRCN model through Keras. The model comes from the paper Long-term Recurrent Convolutional Networks for Visual Recognition and Description. I recently needed to use this model for an experiment and didn’t find much implementation code online, so I’m recording it here for reference.
通过 Keras 实现 LRCN 模型
本文主要介绍了如何通过 Keras 实现 LRCN 模型，模型出自论文 Long-term Recurrent Convolutional Networks for Visual Recognition and Description，最近需要用这个模型做个实验，在网上搜到的实现代码不多，因此这里记录一下，以供参考。
共享键鼠神器 Synergy
发表于 标签 工具使用
最近需要频繁切换使用台式机和笔记本，但是我的小桌子上实在没法同时放得下一个键盘和笔记本 （≧0≦）。哪怕凑合挤下，还得不停在两台电脑之间切换键鼠，因此就想着有没有共享键鼠的方案，结果在网上找到了 Synergy，试了几天后发现这真的是一个共享键鼠的神器。
Introduction and Implementation of WAND Algorithm
发表于 标签 计算广告
This article mainly introduces the principles and implementation of the WAND (Weak AND) algorithm. WAND is a search algorithm applied in scenarios where a query has multiple keywords or tags, and each document also has multiple keywords or tags (such as search engines). Especially when the query contains many keywords or tags, WAND can quickly select the Top n relevant documents. The original paper of the algorithm can be found at Efficient Query Evaluation using a Two-Level Retrieval Process. This article mainly explains the principles of this algorithm and implements it in Python.
Wand 算法介绍与实现
发表于 标签 计算广告
本文主要介绍 Wand (Weak And) 算法的原理和实现, Wand 算法是一个搜索算法，应用在 query 有多个关键词或标签，同时每个 document 也有多个关键词或标签的情形（如搜索引擎）；尤其是在 query 中的关键词或标签较多的时候，通过 Wand 能够快速的选择出 Top n 个相关的 document，算法的原始论文见 Efficient Query Evaluation using a Two-Level Retrieval Process，本文主要讲述这个算法的原理以及通过 python 实现这个算法。
Four Realms of Large-Scale Machine Learning Frameworks
This article is a repost, the original link can be found here, authored by carbon zhang. This article mainly introduces several key concepts and classic papers in distributed machine learning, including data parallelism and model parallelism, schools of distributed frameworks, parameter servers, and the evolution of synchronization protocols. It is well worth reading.
大规模机器学习框架的四重境界
文章为转载，原文链接见这里，作者是 carbon zhang。这篇文章主要介绍了分布式机器学习中的若干重点概念和经典论文，包括数据并行和模型并行、分布式框架的流派、参数服务器以及同步协议的演进等，非常值得一看。
Distributed Machine Learning (4) - Implement Your MapReduce
When mentioning MapReduce, one naturally thinks of Hadoop MapReduce, but MapReduce is just a programming paradigm, and Hadoop MapReduce is a well-known implementation of this paradigm. Actually, MapReduce can be implemented in various ways. This article introduces how to implement a MapReduce program in Linux bash, with both single-machine and multi-machine versions. The original video is here (requires VPN).