不就ideas嘛，谁没有！

20160214

survey of current RDF triple storage systems
survey of semantic web stack inference mechanism
embrace semantic web in giant data processing:
graph computing?
graph database search transformation?
reasoning mechanism modified?

20160215

play Apache Jena with JDBC
play Apache Jena with Text Search
play Apache Jena with Apache Hadoop, finally

20160216

Top Machine Learning, Data Mining, & NLP Books that Every Data Scientist Should Read: 机器学习、数据挖掘、NLP书籍

20160301

Apache Jena jena-elephas-*似乎只是提供了一种RDF中节点、关系等统计信息的方法，

如何执行类似与SPARQL查询、RDF推理？

RDF查询和推理算法的实现需要基于图的存储和处理算法支持。

直接切换为Neo4j或现有Hadoop生态系统的已有图算法框架？

20160305

Notes of 大数据智能里记录了一些图处理算法。

Let's rock and roll.

20160306

Google学术搜索，资源比你想象的还多。
Chrome有个插件“Google Scholar Button”。
Maven仓库搜索页面中已经有SBT依赖了，Scala生态系统正成为一股不可忽视的势力。

20160307

剑桥大学NLIP组S.Teufel《词汇语义学》8讲. Lexical Semantics

1歧义,词义,蕴涵
2wordnet和词义消歧(WSD)的有/无/半监督算法
3词汇的上下文、部分与整体关系,基于wordnet/图的消歧方法
4上下文,语义空间与语义相似
5动词的语义,FrameNet
6比喻(暗喻,反语,幽默)
7形容词的语义倾向,反义词组,情感检测
8: Applications based on lexical semantics.

机器学习入门资源不完全汇总: 2014-10-14版, 好东西传送门编辑整理

20160310

1 大数据的教育产业？ 5980元抢购加入大数据IMF传奇行动！

2 集群调度架构的演变：The evolution of cluster scheduler architectures.

3 Bloom Filters的教程和相关资源： Bloom Filters by Example

4 BSP(Bulk Sysnchronous Parallel) wiki: Bulk synchronous parallel

5 Spark GraphFrame

Introducing GraphFrames

基于Spark DataFrame的图数据库GraphFrame：用Spark SQL查询Graph

20160311

知乎目前常用的自然语言处理开源项目/开发包有哪些？

20160312

1 复杂系统的建模与分析 - a book
Sayama H.. Introduction to the Modeling and Analysis of Complex Systems. 2015.

2 概率图模型导论 - a lecture notes
Koller D., Friedman N. et al. Graphical Models in a Nutshell.

Koller D., Friedman N.. 概率图模型:原理与技术. 2015：清华大学出版社。

20160313

1 O’Reilly 报告：机器智能的未来

这份报告仅能涵盖这么多内容，注意这一点很重要。仅有十篇访谈，远不够详尽：实际上，就每一篇访谈而言，都有几十位其他理论和实践人员通过他们的努力和奉献成功推动该领域发展。这份报告，尽管简短，但是借由这些顶尖人物的双眼，我们得以一睹这一精彩领域。

20160314

1 Training and serving NLP models using Spark MLlib

Our engineering team has built a platform that trains and serves thousands of NLP models, which function in a distributed environment. This allows us to scale out quickly and provide thousands of predictions per second for many clients simultaneously. In this post, we’ll explore the types of problems we’re working to resolve, the processes we follow, and the technology stack we use. This should be helpful for anyone looking to build out or improve their own NLP pipelines.

2 O'Reilly Media Ideas

On our radar: The essential topics and big ideas we’re tracking.

O'Reilly Media跟踪的主题和idea.

20160315

1 大常识知识库中查询技术

Controlling Search in Very large Commonsense Knowledge Bases: A Machine Learning Approach 2016

Very large commonsense knowledge bases (KBs) often have thousands to millions of axioms, of which relatively few are relevant for answering any given query. A large number of irrelevant axioms can easily overwhelm resolution-based theorem provers. Therefore, methods that help the reasoner identify useful inference paths form an essential part of large-scale reasoning systems. In this paper, we describe two ordering heuristics for optimization of reasoning in such systems. First, we discuss how decision trees can be used to select inference steps that are more likely to succeed. Second, we identify a small set of problem instance features that suffice to guide searches away from intractable regions of the search space. We show the efficacy of these techniques via experiments on thousands of queries from the Cyc KB. Results show that these methods lead to an order of magnitude reduction in inference time.

没有下载到原文啊！

2 A survey of MapReduce family system

Sakr S, Liu A, Fayoumi A G. The family of MapReduce and large-scale data processing systems[J]. ACM Computing Surveys (CSUR), 2013, 46(1): 11.

RDF

A.2 MapReduce for Large Scala RDF Processing

Graph

A.3 MapReduce for Large Scala Graph Processing

20160316

1 MOOC深度学习课程列表：课程列表: Deep Learning

2 神经网络Java编程：Neural Network Programming with Java(Packt 2016).

20160317

1 知识表示(Knowledge Representation)的数学背景

Mathematical Background - a revised and extended version of Appendix A from the book Conceptual Structures by John F. Sowa

20160321

1 手写数字K-Mean聚类 (Python)
K-Means Clustering on Handwritten Digits

2 机器学习算法中过拟合、欠拟合问题
Overfitting and Underfitting With Machine Learning Algorithms

3 人工神经网络基础
The Essence of Artificial Neural Networks

20160324

1 统计分析(statistical analysis)的技术和语言

2 机器学习任务的调试(Debugging Machine Learning Tasks)

Unlike traditional programs (such as operating systems or word processors) which have large amounts of code, machine learning tasks use programs with relatively small amounts of code (written in machine learning libraries), but voluminous amounts of data. Just like developers of traditional programs debug errors in their code, developers of machine learning tasks debug and fix errors in their data. However, algorithms and tools for debugging and fixing errors in data are less common, when compared to their counterparts for detecting and fixing errors in code. In this paper, we consider classification tasks where errors in training data lead to misclassifications in test points, and propose an automated method to find the root causes of such misclassifications. Our root cause analysis is based on Pearl's theory of causation, and uses Pearl's PS (Probability of Sufficiency) as a scoring metric. Our implementation, Psi, encodes the computation of PS as a probabilistic program, and uses recent work on probabilistic programs and transformations on probabilistic programs (along with gray-box models of machine learning algorithms) to efficiently compute PS. Psi is able to identify root causes of data errors in interesting data sets.

20160326

1 智能系统中深度神经网络的一个入门教程
A Tutorial on Deep Neural Networks for Intelligent Systems

2 Springer用于概率、统计和机器学习的Python书
Python for Probability, Statistics, and Machine Learning

3 卷积神经网络
Notes on Convolutional Neural Networks

A guide to convolution arithmetic for deep learning Github code

Deep Learning for Computer Vision – Introduction to Convolution Neural Networks

20160327

一些技术性博客

1 AWS AWS Big Data Blog

2 IBM Blogs | IBM Big Data & Analytics Hub

3 Google Cloud GOOGLE CLOUD BIG DATA BLOG

4 more

90+ Active Blogs on Analytics, Big Data, Data Mining, Data Science, Machine Learning

20160330

1 Github中一个深度学习中自然语言处理的资源列表

Deep-Learning-for-NLP-Resources

20160401

1 谷歌容器管理10年记

【编者的话】本文介绍了过去十年谷歌在容器管理方面的实践，包括了Borg，Omega和Kubernetes的历史和架构方面的比较，谷歌在其内部使用容器的概况，以及谷歌试图通过社区来推动其Kubernetes成为容器管理标准的努力。

2 CSDN 大型网站架构

剖析大型网站技术架构模式，深入讲述大型互联网架构设计的核心原理，全面介绍大型网站架构需要的方方面面知识/技术。

20160404

1 偏向于学界的多租户(multi-tenancy)的文章

A generic (and highly academic) discussion around multi-tenancy

2 深度学习教程，即将MIT Press出版

Deep Learning - An MIT Press book in preparation - Ian Goodfellow, Yoshua Bengio and Aaron Courville

The Deep Learning textbook is a resource intended to help students and practitioners enter the field of machine learning in general and deep learning in particular. The book will be available for sale soon, and will remain available online for free.

一些读书笔记： All of Recurrent Neural Networks and LSTM

20160405

1 编写Shell脚本的好的实践

Good practices for writing shell scripts Or how to care about the people reading them

2 Linux系统调用 The Definitive Guide to Linux System Calls

20160406

1 Deep Graphs - a general framework to represent and analyze heterogeneous complex systems across scales

网络图理论

Network theory has proven to be a powerful tool in describing and analyzing systems by modelling the relations between their constituent objects. In recent years great progress has been made by augmenting `traditional' network theory. However, existing network representations still lack crucial features in order to serve as a general data analysis tool. These include, most importantly, an explicit association of information with possibly heterogeneous types of objects and relations, and a conclusive representation of the properties of groups of nodes as well as the interactions between such groups on different scales.

In this paper, we introduce a collection of definitions resulting in a framework that, on the one hand, entails and unifies existing network representations (e.g., network of networks, multilayer networks), and on the other hand, generalizes and extends them by incorporating the above features. To implement these features, we first specify the nodes and edges of a finite graph as sets of properties. Second, the mathematical concept of partition lattices is transferred to network theory in order to demonstrate how partitioning the node and edge set of a graph into supernodes and superedges allows to aggregate, compute and allocate information on and between arbitrary groups of nodes. The derived partition lattice of a graph, which we denote by deep graph, constitutes a concise, yet comprehensive representation that enables the expression and analysis of heterogeneous properties, relations and interactions on all scales of a complex system in a self-contained manner. Furthermore, to be able to utilize existing network-based methods and models, we derive different representations of multilayer networks from our framework and demonstrate the advantages of our representation. We exemplify an application of deep graphs using a real world dataset of precipitation measurements.

20160408

1 演化计算(Evolutionary Computation)

Evolutionary Computation: Theory
Evolutionary Computation: Phenotype
Evolutionary Computation: Genotype
Evolutionary Computation: Loop

20160520

1 大规模和高维数据可视化

论文：Visualizing Large-scale and High-dimensional Data
作者个人主页：Big Data Visualization
Github实现：largeVis

20160526

1 领域实体关系挖掘：InfoQ 特定领域实体关系如何挖掘？ --高手支招

2 算法是否需要大数据的讨论：Algorithms That Learn with Less Data Could Expand AI’s Power

中文译文：一味追求大数据是机器学习的误区，我们的算法所需数据更少且速度更快

3 Hadoop可视化与交互式工具：Hadoop可视化与交互式工具：Zeppelin和Hue

4 算法可视化工具：Algorithm Visualizer

20160530

1 2016需要考察的企业技术：The enterprise technologies to watch in 2016

20160602

已移动到Notes of Ideas