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Training and Visualizing a neural network with R

The development of a neural network is inspired by human brain activities. As such, this type of network is a computational model that mimics the pattern of the human mind. In contrast to this, support vector machines first map input data into a high dimension feature space defined by the kernel function, and find the optimum hyperplane that separates the training data by the maximum margin. In short, we can think of support vector machines as a linear algorithm in a high dimensional space. In this article, we will cover: Training a neural network with neuralnet Visualizing a neural network trained by neuralnet

Stata as Data Analytics Software

In this article by Prasad Kothari , the author of the book Data Analysis with STATA , the overall goal is to cover the STATA related topics such as data management, graphs and visualization and programming in STATA. The article will give a detailed description of STATA starting with an introduction to STATA and Data analytics and then talks about STATA programming and data management. After which it takes you through Data visualization and all the important statistical tests in STATA. Then the article will cover the Linear and the Logistics regression in STATA and in the end it will take you through few analyses like Survey analysis, Time Series analysis and Survival analysis in STATA. It also teaches different types of statistical modelling techniques and how to implement these techniques in STATA.

Saying Hello to Java EE

To develop a scalable, distributed, well-presented, complex, and multi-layered enterprise application is complicated. The development becomes even worse if the developer is not well aware of the software development fundamentals. Instead of looking at a bigger scenario, if we cut it down into parts and later combine them, it becomes easy for understanding as well as for developing. Each technology has some basics which we cannot overlook. Rather, if we overlook them, it will be the biggest mistake; the same is applicable to Java EE. In this article by TejaswiniMandar Jog , author of the book Learning Modular Java Programming , we are going to explore the following: Java EE technologies Why servlet and JSP? Introduction to Spring MVC Creating a sample application through Spring MVC

C compiler, Device Drivers and Useful Developing Techniques

In this article by Rodolfo Giometti , author of the book GNU/Linux Rapid Embedded Programming , in this article we’re going to focusing our attention to the C compiler (with its counter part: the cross-compiler ) and when we have (or we can choose to) to use the native or cross-compilation and the differences between them.

About the Certified OpenStack Administrator Exam

In this article by Matt Dorn , authors of the book Certified OpenStack Administrator Study Guide , we will learn and understand how we can pass the Certified OpenStack Administrator exam successfully!

WebLogic Server

In this article by Adrian Ward , Christian Screen and Haroun Khan , the author of the book Oracle Business Intelligence Enterprise Edition 12c - Second Edition , will talk a little more in detail about the enterprise application server that is at the core of Oracle Fusion Middleware, WebLogic. Oracle WebLogic Server is a scalable, enterprise-ready Java Platform Enterprise Edition (Java EE) application server. Its infrastructure supports the deployment of many types of distributed applications. It is also an ideal foundation for building service-oriented applications (SOA). You can already see why BEA was a perfect acquisition for Oracle years ago. Or, more to the point, a perfect core for Fusion Middleware.

About Java Virtual Machine – JVM Languages

In this article by Vincent van der Leun , the author of the book, Introduction to JVM Languages , you will learn the history of the JVM and five important languages that run on the JVM.

Testing in Agile Development and the State of Agile Adoption

In this article written by Renu Rajani , author of the book Testing Practitioner Handbook , we will discuss agile development. Organizations are increasingly struggling to reach the right level of quality versus speed. Some key issues with traditional development and testing include the following: Excessively long time to market for products and applications Inadequate customer orientation and regular interaction Over-engineered products--most of the features on a product or application may not be used High project failure rate ROI below expectation Inability to respond quickly to change Inadequate software quality

Replication Solutions in PostgreSQL

In this article by Chitij Chauhan , Dinesh Kumar , the authors of the book PostgreSQL High Performance Cookbook , we will talk about various high availability and replication solutions including some popular third-party replication tool like Slony.

The Interface

 In this article by Tim Woodruff , authors of the book Learning ServiceNow , No matter what software system you're interested in learning about, understanding the interface is likely to be the first step toward success. ServiceNow is a very robust IT service management tool, and has an interface to match. Designed both to be easy to use, and to support a multitude of business processes and applications (both foreseen and unforeseen), it must be able to bend to the will of the business, and be putty in the hands of a capable developer.

Members Inheritance and Polymorphism

In this article by Gastón C. Hillar , the author of the book Java 9 with JShell , we will learn about one of the most exciting features of object-oriented programming in Java 9: polymorphism. We will code many classes and then we will work with their instances in JShell to understand how objects can take many different forms. We will: Create concrete classes that inherit from abstract superclasses Work with instances of subclasses Understand polymorphism Control whether subclasses can or cannot override members Control whether classes can be subclassed Use methods that perform operations with instances of different subclasses

Learn from Data

In this article by Rushdi Shams , the author of the book Java Data Science Cookbook , we will cover recipes that use machine learning techniques to learn patterns from data. These patterns are at the centre of attention for at least three key machine-learning tasks: classification, regression, and clustering. Classification is the task of predicting a value from a nominal class. In contrast to classification, regression models attempt to predict a value from a numeric class.

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