機器學習--超參數相關設定範圍

http://blog.kaggle.com/2016/07/21/approaching-almost-any-machine-learning-problem-abhishek-thakur/



http://machinelearningmastery.com/configure-gradient-boosting-algorithm/

Wikipedia Factoid Bot

Post 1: Intro factoid bot demo plus download and configure code
Post 2: Identify famous people as entities using Alchemy Language
Post 3: Initialize the factoid bot’s connection to Watson Conversation
Post 4: You are here
Post 5: Extract answers from DBpedia (Wikipedia)
Post 6: Finalize the conversation flow

Working Demo: Factoid bot running on Bluemix
Source Code: NodeJS factoid bot on Github

機器學習、深度學習與自然語言處理領域推薦的書籍列表

https://segmentfault.com/a/1190000008598352

機器學習

• 2007 - Pattern Recognition And Machine Learning【Book】 : The book is suitable for courses on machine learning, statistics, computer science, signal processing, computer vision, data mining, and bioinformatics.
• 2012 - Machine Learning A Probabilistic Perspective 【Book】 : This textbook offers a comprehensive and self-contained introduction to the field of machine learning, a unified, probabilistic approach. The coverage combines breadth and depth, offering necessary background material on such topics as probability , optimization, and linear algebra as well as discussion of recent developments in the field, including conditional random fields, L1 regularization, and deep learning.
• 2012 -李航:統計方法學：李航老師的這本書偏優化和推倒，推倒相應算法的時候可以參考這本書。
• 2014 - DataScience From Scratch【Book】 : In this book, you'll learn how many of the most fundamental data science tools and algorithms work by implementing them from scratch.
• 2015 - Python Data Science Handbook【Book】 :Jupyter Notebooks for the Python Data Science Handbook
• 2015 - Data Mining, The Textbook【Book】 : This textbook explores the different aspects of data mining from the fundamentals to the complex data types and their applications, capturing the wide diversity of problem domains for data mining issues.
• 2016 -周志華機器學習【Book】：周志華老師的這本書非常適合作為機器學習入門的書籍，書中的例子十分形象且簡單易懂。
• University of Illinois at Urbana-Champaign:Text Mining and Analytics【Course】
• 台大機器學習技法【Course】
• 斯坦福機器學習課程【Course】
• CS224d: Deep Learning for Natural Language Processing【Course】
• Unsupervised Feature Learning and Deep Learning【Course】 :來自斯坦福的無監督特徵學習與深度學習系列教程
• 史上最全的机器学习资料（下）涵蓋24種編程語言的機器學習的框架、庫以及其他相關資料https://my.oschina.net/freegodly/blog/740027 

深度學習

• 2015-The Deep Learning Textbook【Book】 :中文譯本這裡，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 online version of the book is now complete and will remain available online for free.
• Stanford Deep Learning Tutorial【Book】 : This tutorial will teach you the main ideas of Unsupervised Feature Learning and Deep Learning. By working through it, you will also get to implement several feature learning/deep learning algorithms, get to see them work for yourself , and learn how to apply/adapt these ideas to new problems.
• Neural Networks and Deep Learning【Book】 : Neural Networks and Deep Learning is a free online book. The book will teach you about: (1) Neural networks, a beautiful biologically-inspired programming paradigm which enables a computer to learn from observational data. (2) Deep learning, a powerful set of techniques for learning in neural networks
• Practical Deep Learning For Coders 【Course】 :七週的免費深度學習課程，學習如何構建那些優秀的模型。
• Oxford Deep NLP 2017 course【Course】 : This is an advanced course on natural language processing. Automatically processing natural language inputs and producing language outputs is a key component of Artificial General Intelligence.

自然語言處理

• 2016 - CS224d: Deep Learning for Natural Language Processing【Course】
• 2017 - Oxford Deep NLP 2017 course【Course】
• [2015 - Text Data Management and Analysis【Book】](): A Practical Introduction to Information Retrieval and Text Mining
• DL4NLP-Deep Learning for NLP resources【Resource】

Semantic Web vs. semantic technology

Semantic Web Technologies course 
http://www.meteck.org/SWT.html

Foundations of Semantic Web Technologies (許多課程投影片)
http://www.semantic-web-book.org/page/Slides

• Knowledge Representation for the Semantic Web, course at the Department of Computer Science and Engineering, Wright State University, Dayton, Ohio, winter quarter 2012. Covers RDF and OWL in depth.

Transformation of ZML schemas and XML data to RDF/OWL
http://topquadrantblog.blogspot.tw/2011/09/living-in-xml-and-owl-world.html


XML SchemaPlus Specification
This document presents a specification of XML SchemaPlus (XSP) for describing the layout of an XML document in such a way that RDF/OWL semantics can be retained.



Semantic University
http://www.cambridgesemantics.com/semantic-university/semantic-web-vs-semantic-technology#

Some examples of semantic technologies include natural language processing (NLP), data mining, artificial intelligence (AI), category tagging, and semantic search.

Semantic Web technologies include:

• a flexible data model (RDF),
• schema and ontology languages for describing concepts and relationships (RDFS and OWL),
• a query language (SPARQL),
• a rules language (RIF),
• a language for marking up data inside Web pages (RDFa),
• and more.

• Semantic Web Landscape
  • Getting Started: Understanding Semantic Technologies
  • Semantic Technologies Compared
  • Semantic Technologies Applied

• Technical Lesson Tracks
  • Learn RDF
  • Learn OWL and RDFS
  • Learn SPARQL
  • Semantic Web Design Patterns

• About Semantic University
  • Contact Semantic University
  • 
    

Deep Learning for Named Entity Recognition

some interesting recent (2015-2016) papers related to that:

http://deeplearningkit.org/2016/04/23/deep-learning-for-named-entity-recognition/

1. Capturing Semantic Similarity for Entity Linking with Convolutional Neural Networks – authors: M Francis
2. Entity Attribute Extraction from Unstructured Text with Deep Belief Network – authors: B Zhong, L Kong, J Liu
3. Learning Word Segmentation Representations to Improve Named Entity Recognition for Chinese Social Media – authors: N Peng, M Dredze
4. Biomedical Named Entity Recognition based on Deep Neutral Network – authors: L Yao, H Liu, Y Liu, X Li, MW Anwar
5. Shared tasks of the 2015 workshop on noisy user-generated text: Twitter lexical normalization and named entity recognition – authors: T Baldwin, MC de Marneffe, B Han, YB Kim, A Ritter…
6. Semi-Supervised Approach to Named Entity Recognition in Spanish Applied to a Real-World Conversational System – authors: SS Bojórquez, VM González
7. Boosting Named Entity Recognition with Neural Character Embeddings – authors: C dos Santos, V Guimaraes, RJ Niterói, R de Janeiro
8. Exploring Recurrent Neural Networks to Detect Named Entities from Biomedical Text – authors: L Li, L Jin, D Huang
9. Entity-centric search: querying by entities and for entities – authors: M Zhou
10. Automatic Entity Recognition and Typing from Massive Text Corpora: A Phrase and Network Mining Approach – authors: X Ren, A El
11. Boosting Named Entity Recognition with Neural Character Embeddings – authors: CN Santos, V Guimarães
12. Named Entity Recognition in Chinese Clinical Text Using Deep Neural Network. – authors: Y Wu, M Jiang, J Lei, H Xu
13. Context-aware Entity Morph Decoding – authors: B Zhang, H Huang, X Pan, S Li, CY Lin, H Ji, K Knight…
14. Training word embeddings for deep learning in biomedical text mining tasks – authors: Z Jiang, L Li, D Huang, L Jin
15. Entity Attribute Extraction from Unstructured Text with Deep Belief Network – authors: B Zhong, L Kong, J Liu
16. Building Text-mining Framework for Gene-Phenotype Relation Extraction using Deep Leaning – authors: D Jang, J Lee, K Kim, D Lee
17. Text Mining in Social Media for Security Threats – authors: D Inkpen
18. Text Understanding from Scratch – authors: X Zhang, Y LeCun
19. Syntax-based Deep Matching of Short Texts – authors: M Wang, Z Lu, H Li, Q Liu
20. PTE: Predictive Text Embedding through Large-scale Heterogeneous Text Networks – authors: J Tang, M Qu, Q Mei
21. Automatic Entity Recognition and Typing from Massive Text Corpora: A Phrase and Network Mining Approach – authors: X Ren, A El
22. Domain-Specific Semantic Relatedness from Wikipedia Structure: A Case Study in Biomedical Text – authors: A Sajadi, EE Milios, V Kešelj, JCM Janssen
23. Deep Unordered Composition Rivals Syntactic Methods for Text Classification – authors: M Iyyer, V Manjunatha, J Boyd
24. Representing Text for Joint Embedding of Text and Knowledge Bases – authors: K Toutanova, D Chen, P Pantel, H Poon, P Choudhury…
25. In Defense of Word Embedding for Generic Text Representation – authors: G Lev, B Klein, L Wolf

Notes on Conversational Interfaces

https://quip.com/VjJFAFmzJ35P

Knowledge base systems

Knowledge-based Artificial Intelligence
http://www.mkbergman.com/1816/knowledge-based-artificial-intelligence/
A recent interview with a noted researcher, IEEE Fellow Michael I. Jordan, Pehong Chen Distinguished Professor at the University of California, Berkeley, provided a downplayed view of recent AI hype. Jordan was particularly critical of AI metaphors to real brain function and took the air out of the balloon about algorithm advances, pointing out that most current methods have roots that are decades long [1]. In fact, the roots of knowledge-based artificial intelligence (KBAI), the subject of this article, also extend back decades.


一些和Knowledge base systems相關的資料整理網站
http://appleiphones.org/images/knowledge+base+systems