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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
Stochastic Signal Analysis is a field of science concerned with the processing, modification and analysis of (stochastic) signals.
Anyone with a background in Physics or Engineering knows to some degree about signal analysis techniques, what these technique are and how they can be used to analyze, model and classify signals.
Data Scientists coming from a different fields, like Computer Science or Statistics, might not be aware of the analytical power these techniques bring with…Continue
Added by ahmet taspinar on April 29, 2018 at 9:00am — No Comments
It is crucial to ask the right questions and/or understand the problem, prior to beginning data analysis. Below is a list of 20 questions you need to ask before delving into analysis:
Added by Cynthia Clare on May 23, 2018 at 8:30pm — No Comments
Let us consider the following equation:
PHP, known as the most popular server-side scripting language in the world, has evolved a lot since the first inline code snippets appeared in static HTML files.
These days developers need to build complex websites and web apps, and above a certain complexity level it can take too much time and hassle to always start from scratch, hence came the need for a more structured natural way of development. PHP frameworks provide developers with an adequate solution for that.
Added by Rajveer Singh Rathore on July 30, 2018 at 4:30am — No Comments
Mathematical Olympiads are popular among high school students. However, there is nothing similar for college students, except maybe IMC. Even IMC is not popular. It focuses mostly on the same kind of problems as high school Olympiads, and you can not participate if you are over 23 years old. In addition, it is organized by country, as opposed to globally, thus favoring countries with a large population. Topics such as…Continue
Added by Vincent Granville on May 25, 2018 at 9:00am — No Comments
Predictive analytics uses current and historical data in order to determine the probability of a particular outcome. This is a particularly powerful approach when it is applied to medical diagnosis. In an effort to reduce misdiagnosis, historical data of former patient’s symptoms may be applied to the assessment of a new patient.
While doctors are the ultimate experts and decision-makers, using predictive analytics as a means of establishing precedent for…Continue
Added by Goli Tajadod on May 22, 2018 at 2:30am — No Comments
This article is intended for practitioners who might not necessarily be statisticians or statistically-savvy. The mathematical level is kept as simple as possible, yet I present an original, simple approach to test for randomness, with an interesting application to illustrate the methodology. This material is not something usually discussed in textbooks or classrooms (even for statistical students), offering a fresh perspective, and out-of-the-box tools that are useful in many contexts, as…Continue