A Data Science Central Community
Organizations across industries are adopting graph analytics to reinforce their anti-fraud programs. In this post, we examine three types of fraud graph analytics can help investigators combat: insurance fraud, credit card fraud, VAT fraud.
In many areas, fraud investigators have at their disposal large datasets in which clues are hidden. These clues are left behind by…
Added by Elise Devaux on January 22, 2019 at 12:30am — No Comments
Extract from the upcoming 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. To subscribe, …Continue
Added by Vincent Granville on January 20, 2019 at 12:15pm — No Comments
This article was written by Ajit Joakar.
In this longish post, I have tried to explain Deep Learning starting from familiar ideas like machine learning. This approach forms a part of my forthcoming book. I have used this approach in my teaching. It is based on ‘learning by exception,' i.e. understanding one concept and it’s limitations and then understanding how the subsequent concept…Continue
Added by Vincent Granville on January 16, 2019 at 9:48am — No Comments
Why is graph visualization so important? How can it help businesses sifting through large amounts of complex data? We explore the answer in this post through 5 advantages of graph visualization and different use cases.
Also called network, a graph is a collection of nodes (or vertices) and edges (or links). Each node represents a single data point (a person, a phone number, a transaction) and each edge represents how two nodes…Continue
Added by Elise Devaux on January 11, 2019 at 9:25am — No Comments
Summary: Here are our 5 predictions for data science, machine learning, and AI for 2019. We also take a look back at last year’s predictions to see how we did.
Added by Vincent Granville on December 20, 2018 at 6:30pm — No Comments
We are in the process of writing and adding new material (compact eBooks) exclusively available to our members, and written in simple English, by world leading experts in AI, data science, and machine learning. In the upcoming months, the following will be added:
Added by Vincent Granville on December 1, 2018 at 6:26pm — No Comments
Summary: This may be the golden age of deep learning but a lot can be learned by looking at where deep neural nets aren’t working yet. This can be a guide to calming the hype. It can also be a roadmap to future opportunities once these barriers are behind us. The full article is accessible here, below is a…Continue
Added by Vincent Granville on November 21, 2018 at 10:00am — No Comments
From detecting anomalies to understanding what are the key elements in a network, or highlighting communities, graph analytics reveal information that would otherwise remain hidden in your data. We will see how to integrate your graph analytics with Linkurious Enterprise to detect and investigate insights in your connected data.
Added by Elise Devaux on October 4, 2018 at 9:30am — No Comments
Summary: There are several approaches to reducing the cost of training data for AI, one of which is to get it for free. Here are some excellent sources.
Recently we wrote that training data (not just data in general) is the new oil. It’s the difficulty and expense of acquiring labeled training data that causes many deep learning projects to be abandoned.
It also matters a great deal just how good you want your new deep learning app to be. A 2016 study by…Continue
Added by Vincent Granville on October 3, 2018 at 10:49am — No Comments
We all know that deep learning algorithms improve the accuracy of AI applications to great extent. But this accuracy comes with requiring heavy computational processing units such as GPU for developing deep learning models. Many of the machine learning developers cannot afford GPU as they are very costly and find this as a roadblock for learning and developing Deep learning applications. To help the AI, machine learning developers Google has released…
Added by suresh kumar Gorakala on October 1, 2018 at 9:07am — No Comments
Previously, we saw how unsupervised learning actually has built-in supervision, albeit hidden from the user.
In this post we will see how supervised and unsupervised learning algorithms share more in common than the textbooks would suggest. As a matter of fact, both classes can use identical…Continue
Added by Danko Nikolic on September 23, 2018 at 1:34pm — No Comments
Guest blog post by Zied HY. Zied is Senior Data Scientist at Capgemini Consulting. He is specialized in building predictive models utilizing both traditional statistical methods (Generalized Linear Models, Mixed Effects Models, Ridge, Lasso, etc.) and modern machine learning techniques (XGBoost, Random Forests, Kernel Methods, neural networks, etc.). Zied run some workshops for university students (ESSEC, HEC, Ecole polytechnique) interested in Data…Continue
Added by Vincent Granville on September 21, 2018 at 12:00pm — No Comments
Summary: The role of Analytics Translator was recently identified by McKinsey as the most important new role in analytics, and a key factor in the failure of analytic programs when the role is absent.
The role of Analytics Translator was recently identified by McKinsey as the most important new role in…Continue
Added by Vincent Granville on September 12, 2018 at 5:30pm — No Comments
You won't learn this in textbooks, college classes, or data camps. Some of the material in this article is very advanced yet presented in simple English, with an Excel implementation for various statistical tests, and no arcane theory, jargon, or obscure theorems. It has a number of applications, in finance in particular. This article covers several topics under a unified approach, so it was not easy to find a title. In particular, we discuss:
Added by Vincent Granville on September 10, 2018 at 9:07pm — No Comments
Full title: Applied Stochastic Processes, Chaos Modeling, and Probabilistic Properties of Numeration Systems. Published June 2, 2018. Author: Vincent Granville, PhD. (104 pages, 16 chapters.)
This book is intended for professionals in data science, computer science, operations research, statistics, machine learning, big data, and mathematics. In 100 pages, it covers many new topics, offering a fresh perspective on the subject. It is accessible to…Continue
Added by Vincent Granville on September 8, 2018 at 11:16am — No Comments
Join the largest community of machine learning (ML), deep learning, AI, data science, business analytics, BI, operations research, mathematical and statistical professionals: Sign up here. If instead, you are only interested in receiving our newsletter, you can subscribe here. There is no…Continue
Added by Vincent Granville on September 8, 2018 at 11:14am — No Comments
Let us consider the following equation:
Here is Rafael Knuth's story.
In 1992, I entered the job market and landed a job as an advertising copywriter for McDonald’s. I was tasked with ideating radio, TV and print advertisements to curb burger, fries and soft drink sales. The internet did not exist in the public domain back then, and my first laptop was actually a mechanical type writer. Around 2000, I became a freelance…Continue
Added by Vincent Granville on August 30, 2018 at 5:00pm — No Comments
Here is our selection of recently featured articles and resources:
Featured Resources and Technical ContributionsContinue
Added by Vincent Granville on August 25, 2018 at 6:45pm — No Comments
For a person being from a non-statistical background the most confusing aspect of statistics, are always the fundamental statistical tests, and when to use which. This blog post is an attempt to mark out the difference between the most common tests, the use of null value hypothesis in these tests and outlining the conditions under which a particular test should be used.
Null Hypothesis and Testing
Before we venture on the difference between different tests, we…Continue
Added by Vincent Granville on August 22, 2018 at 11:00am — No Comments