Book Image

Building Websites with VB.NET and DotNetNuke 4

Book Image

Building Websites with VB.NET and DotNetNuke 4

Overview of this book

DotNetNuke is an open-source Content Management System and web application framework. DotNetNuke has taken the Microsoft world by storm and now at version 4, its community has grown to over 200,000 users. This book covers virtually everything you need to know to get your DotNetNuke website up and running. Concisely written and with clear explanations, this book is covers installation, administration, deployment, site creation and all of the basic built in DotNetNuke modules. For developers, chapters on the core architecture, skinning and custom modules, including coverage of the DAL+, give you the skills to customize and extend your site. The book starts off by giving you a deep understanding of working with basic DotNetNuke sites, guiding you through the features and giving you the confidence to create and manage your site. After that, you will journey to the heart of DotNetNuke, and learn about its core architecture. Always concise, relevant and practical, you will find out what makes DotNetNuke tick, and from there, you will be ready to customize DotNetNuke. Developers will enjoy the detailed walkthrough of creating a new custom modules. Special emphasis is given to the DAL+, an extended feature set of the DotNetNuke Data Access Layer (DAL). You will see how to create custom modules with the DAL+, and invigorate your module development. Web designers will enjoy the material on skinning, helping them to create a new look for their site. You will learn about creating new skins, and packaging them up for easy deployment. You will master all of this as you leap into the development of a DotNetNuke 4 site.
Table of Contents (15 chapters)
Free Chapter
1
Section 1 – Introduction to Graph Machine Learning
4
Section 2 – Machine Learning on Graphs
8
Section 3 – Advanced Applications of Graph Machine Learning

Providing a quick overview of a dataset

To show you how to process a corpus of documents with the aim of extracting relevant information, we will be using a dataset derived from a well-known benchmark in the field of NLP: the so-called Reuters-21578. The original dataset includes a set of 21,578 news articles that were published in the financial Reuters newswire in 1987, which were assembled and indexed in categories. The original dataset has a very skewed distribution, with some categories appearing only in the training set or in the test set. For this reason, we will use a modified version, known as ApteMod, also referred to as Reuters-21578 Distribution 1.0, that has a smaller skew distribution and consistent labels between the training and test datasets.

Even though these articles are a bit outdated, the dataset has been used in a plethora of papers on NLP and still represents a dataset that's often used for benchmarking algorithms.

Indeed, Reuters-21578 contains enough...