This is a new series, featuring forum questions (new and old) that are still popular today. These questions were selected manually based on popularity, removing outdated material. The entire series consists of about 160 questions -- most with answers, sometimes several answers. We intend to publish a new set every two weeks or so. The previous edition was posted …

At 280 pages, the report is too long to cover in detail, but we’ve pulled out the most salient points for you below.

Main Points

• Banks will need to hire excellent data scientists who also understand how markets work
• Machines are best equipped to make trading decisions in the short and medium…

By now anyone who reads virtually any trade magazine has been hearing incessantly about how machine learning is going to transform their industry in profound ways. Marketers will be able to read potential customers’ minds, farms will produce unprecedented yields, doctors will be able to stem diseases before they begin to form. And of course, we’ve all heard how machine learning will eventually take our jobs. It may very well be said of machine learning that there never have…

By Bart Baesens, Véronique Van Vlasselaer and Wouter Verbeke

The importance and need for effective fraud detection and prevention systems is highlighted by some recent numbers which give an indication of the estimated size and the financial impact of fraud:

• A typical organization loses 5% of its revenues to fraud each year (www.acfe.com)
• The total cost of insurance fraud…

Detect fraud earlier to mitigate loss and prevent cascading damage

Fraud Analytics Using Descriptive, Predictive, and Social Network Techniques is an authoritative guidebook for setting up a comprehensive fraud detection analytics solution. Early detection is a key factor in mitigating fraud damage, but it involves more specialized techniques than detecting fraud at the more advanced stages. This invaluable guide details both the theory and technical aspects of these…

Machine Learning, thinking systems, expert systems, knowledge engineering, decision systems, neural networks - all synonymous loosely woven words in the evolving fabric of Artificial Intelligence. Of these Machine Learning (ML) and Artificial Intelligence (AI) are often debated and used interchangeably. broadly speaking AI can be termed as a futuristic state of self aware smart learning machines in true sense, but for all practical purposes we deal more often with ML at present.

In…

Guest blog post by Chirag Tumar.

Any new technology is not successful until it is embraced and used to its potential. Machine learning is no exception to this rule and its success or to say its ability can be gauged by the trends that exist. Machine learning is already a hot technology at the moments and it seems to have a promising future. At present, it…

This article was written by Ray of RayOnStorage Blog. Original title: Compressing information through the information bottleneck during deep learning.…

Summary:  This is the third in our series on chatbots.  In this installment we’ll look at the best practice dos and don’ts as described by a number of successful chatbot developers.

In our first article we covered the chatbot basics…

In today’s world, a successful organization has to achieve data and analytics competencies to stay…

Contents

1. Introduction

2. The Four Pillars of Success        in Analytics

3. Analytics…

In some applications, using the standard precision in your programming language of choice, may not be enough, and can lead to disastrous errors. In some cases, you work with a library that is supposed to provide very high precision, when in fact the library in question does not work as advertised. In some cases, lack of precision results in obvious problems that are easy to spot, and in some cases, everything seems to be working fine and you are not aware that your simulations are completely…

Summary

In machine learning, a convolutional neural network (CNN, or ConvNet) is a class of neural networks that has successfully been applied to image recognition and analysis. In this project I've approached this class of models trying to apply it to stock market prediction, combining stock prices with sentiment analysis. The implementation of the network has been made…

Monday newsletter published by Data Science Central. Previous editions can be found here.  The contribution flagged with a + is our selection for the picture of the week.

Announcement

• Use data to drive the future of business—and your career through…

Artificial Intelligence on the Horizon

The Future of Healthcare

(Second in a Series)

Discussed much more thoroughly in the…

Here is our selection of featured articles and resources posted since Monday:

• AI In Telecom: Intelligent Operations is the New Norm 
• The 3 Phases of Digital Transformation …

Introduction

This post is designed show the internal changes of an Artificial Neural Network (ANN/NN), it shows the outputs of the neurons from the beginning of a Backpropagation algorithm to convergence.

The hope is for a better understanding of why we use a second hidden layer, local minimums, to how many internal nodes are required and their impact on the final solution.

The Dataset…