A Data Science Central Community
Graph are meant to be seen
The third layer of graph technology that we discuss in this article is the front-end layer, the graph visualization one. The visualization of information has been the support of many types of analysis, including Social Network Analysis. For decades, visual representations have helped researchers,…
Added by Elise Devaux on April 9, 2019 at 4:00am — No Comments
The emergence of alternative data as a key enabler in expanding credit delivery and financial inclusion is unmistakable.
The saying that the only thing that is constant is change, is attributed to Heraclitus, the Greek Philosopher. This is so very relevant today in the way lenders use technology and scoring solutions to understand the credit worthiness of applicants. Credit Risk Management has come a long way from the days when banks used just one credit score cut off to…Continue
Added by Naagesh Padmanaban on March 25, 2019 at 11:15pm — No Comments
I present here some innovative results from my most recent research on stochastic processes. chaos modeling, and dynamical systems, with applications to Fintech, cryptography, number theory, and random number generators. While covering advanced topics, this article is accessible to professionals with limited knowledge in statistical or mathematical theory. It introduces new material not covered in my recent book (available …Continue
Added by Vincent Granville on March 21, 2019 at 7:30am — No Comments
Graph analytics frameworks consist of a set of tools and methods developed to extract…Continue
Added by Elise Devaux on February 27, 2019 at 5:00am — No Comments
This is another interesting problem, off-the-beaten-path. It ends up with a formula to compute the integral of a function, based on its derivatives solely.
For simplicity, I'll start with some notations used in the context of matrix theory, familiar to everyone: T(f) = g, where f and g are vectors, and T a square matrix. The notation T(f) represents the product between the matrix T, and the vector f. Now, imagine that the…Continue
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
A passionate customer always provides feedback about his favorite product if it touches his emotional chord.
Product review contains wealth of information. Analyzing the review texts can unearth many hidden data points about the customer and the product. Such insights can help grow the business and gain revenue.
Lets look into a specific example. …Continue
Added by Kaniska Mandal on January 24, 2019 at 3:30pm — 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
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
Figure 1. Scatter plot of word embedding coordinates (coordinate #3 vs. coordinate #10). You can see that semantically related words are close to each other.
This blog post is an extract from chapter 6 of the book “…Continue
Added by Rosaria Silipo on May 7, 2018 at 12:00am — 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
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
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
For decades, the intelligence community has been collecting and analyzing information to produce timely and actionable insights for intelligence consumers. But as the amount of information collected increases, analysts are facing new challenges in terms of data processing and analysis. In this article, we explore the possibilities that graph technology is offering for intelligence analysis.
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
This picture speaks more than words. It explains the concept or false positive and false negative, that is, what is referred to by statisticians as Type I and Type II errors.
Other great pictures summarizing data science and statistical concepts, can be found…Continue
Added by Vincent Granville on August 10, 2017 at 5:17pm — No Comments
There is no need to get confused with multiple linear regression, generalized linear model or general linear methods. The general linear model or multivariate regression model is a statistical linear model and is written as Y = XB + U.
Usually, a linear model includes a number of different statistical models such as ANOVA, ANCOVA, MANOVA, MANCOVA, ordinary linear regression, t-test and F-test. The GLM is a generalization of multiple…
Here we discuss an application of HPC (not high performance computing, instead high precision computing, which is a special case of HPC) applied to dynamical systems such as the logistic map in chaos theory. defined as X(k) = 4 X(k) (1 - X(k-1)).
For all these systems, the loss of precision propagates exponentially, to the point that after 50 iterations, all generated values are completely wrong. Tons of articles have been written on this subject - none of them acknowledging the…Continue
Added by Vincent Granville on November 13, 2017 at 7:00pm — No Comments
One of the first lessons you’ll receive in machine learning is that there are two broad categories: supervised and unsupervised learning. Supervised learning is usually explained as the one to which you provide the correct answers, training data, and the machine learns the patterns to apply to new data. Unsupervised learning is (apparently) where the machine figures out the correct answer on its own.
Supposedly, unsupervised learning can discover something new that has not been found…Continue
Added by Danko Nikolic on February 14, 2018 at 1:00pm — No Comments