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All Blog Posts Tagged 'dsc_graph' (19)

Citizen data scientists are a good thing, but are they the only thing?

Data rules the world and, as a result, we want data to drive all the big decisions about how the world works because we believe in rationality, evidence, and science. But most organizations struggle to leverage that data to its competitive advantage. It’s probably even accurate to say that, statistically speaking, almost all organizations fail…

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Added by Kendall Clark on February 17, 2021 at 11:47am — No Comments

AI DataSense Technology

Artificial Intelligence is changing how data management and analysis is handled in the age of big data. Organizations are generating more data than ever before and the process of accessing data using traditional methods is not only time consuming but also poses a threat to the data sets. To handle this challenge, AI changes the way we view data in…

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Added by Indhu on February 12, 2021 at 9:30am — No Comments

Introducing Knowledge Graphs into Organizations

A little soup is quickly cooked. But if many chefs are working together on a larger menu that will eventually be appreciated by a banquet of guests, good preparation is key. In this article we will focus on the preparation phase and outline what it means to introduce…

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Added by Andreas Blumauer on February 9, 2021 at 4:00am — No Comments

How 360-degree customer view helps your business?

Nowadays, the success of many businesses is primarily attributed to good relationships with their customers. That is, having a comprehensive understanding of what customer needs and availing it to satisfy their needs as well as achieve the business goals.  n Mathematics, 360 degrees is a full rotation that is, there are no blind spots.

Similarly, in…

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Added by Indhu on February 7, 2021 at 2:43am — No Comments

Network Graph Visualizations with DOT

Network graphs play a large part in both computing and data science, and they are essential for working with (and visualizing) both semantic graphs and property graphs. Nearly thirty years ago, AT&T produced a set of libraries called graphviz which were…

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Added by Kurt A Cagle on November 29, 2020 at 2:00pm — No Comments

Managing Data in Massive-Scale Vector Search Engine



How data management is done in Milvus…

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Added by Kate Shao on June 8, 2020 at 11:27pm — No Comments

Information Theory (Turbo Codes) & Bayesian Networks

An error correcting code (ECC) is a way of controlling errors in data that is being transmitted over an unreliable or noisy communication channel. In an ECC, the sender encodes the message with redundant information. The receiver is able to detect a limited number of errors, and to correct these errors without retransmission.

Turbo codes are a class of ECC that approach very closely the theoretical…

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Added by Robert R. Tucci on June 4, 2020 at 4:04pm — No Comments

Deploy Your First Serverless AWS ML Solution Fast

Introduction to SageMaker

I’ve been working with AWS SageMaker for a while now and have enjoyed great success. Creating and tuning models, architecting pipelines to support both model development and real-time inference, and data lake formation have all been made easier in my opinion.  AWS has proven to be an all encompassing solution for machine learning use cases, both batch and real-time, helping me decrease time to…

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Added by Jeff Diaz on June 2, 2020 at 12:00pm — No Comments

The Easiest Way to Search Among 1 Billion Image Vectors And Why Vector Search Is Important?

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Added by Kate Shao on May 26, 2020 at 6:00pm — No Comments

Differential ML on TensorFlow and Colab

Brian Huge and I just posted a working paper following six months of…

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Added by Antoine Savine on May 25, 2020 at 11:30am — No Comments

Milvus: A big leap to scalable AI search engine

The challenge with data search

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Added by Kate Shao on May 22, 2020 at 11:00pm — No Comments

What are “Recurrent Neural Networks”, and how it is different than “Convolutional Neural Networks”

CNN and RNN are amongst most important algorithm of Neural Network family, also they differ in their network process and solving problems.

So talking about their differences:

CNN are used to solve classification and regression problems and RNN are used to solve sequence information.

CNN are used for 2D image…

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Added by Sameer Nigam on May 21, 2020 at 12:00pm — No Comments

Random Forest Classification explained in detail and developed in R

In supervised machine learning algorithms, Random Forest stands apart as it is arguably the most powerful classification model. When Microsoft developed their X-box game which enables you to play as per the movement of your posture, they used Random Forest over any other machine learning algorithm and over ANN (Advanced Neural Networks) as well !

I am assuming that if you are reading this blog, you are well versed with Decision Tree Classification. However, if you are not, need not…

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Added by Sourav Kumar Das on May 10, 2020 at 1:30am — No Comments

ELAINE use case – Improving statistical prediction on financial market with Symbolic Logic

In my previous post, I introduced the ELAINE Community Tool that can be used to discover variables from textual communications.

 

Statistical predictors work well for charting the course of economic activities based on macro factors that elicit supply and demand if the market environment is within a close proximity of a pattern that resembles previous economic cycles. Unfortunately, in today’s geo-political environment, many of these new variables are injecting forces that…

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Added by Sing Koo on March 9, 2020 at 4:30am — 1 Comment

Implementing Knowledge Graphs in Enterprises - Some Tips and Trends

Tips

  1. Don't try to put the cart before the horse: realize that efficient data preparation (and thus interoperable standards) and data quality, especially in the enterprise environment, are a basic requirement for…
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Added by Andreas Blumauer on May 21, 2019 at 5:33am — No Comments

Six Core Aspects of Semantic AI

  1. Hybrid approach: Semantic AI is the combination of methods derived from symbolic AI and statistical AI. Virtuously playing the AI piano means that for a given use case various stakeholders, not only data scientists, but also process owners or subject matter experts, choose from available methods and tools, and collaboratively develop workflows that are most likely a good fit to tackle the underlying problem. For example, one can combine entity extraction based on…
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Added by Andreas Blumauer on May 14, 2018 at 4:30am — 2 Comments

Google's Knowledge Graph Identifies your Medical Symptoms

This article was written by Lance Whitney.

An update to Google's mobile app and website lets you search for symptoms and receive a list of possible conditions and other details. You can now ask Google to help diagnose what ails you.…

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Added by Amelia Matteson on March 26, 2018 at 11:00am — No Comments

Why Cognitive Systems should combine Machine Learning with Semantic Technologies

Will Artificial Intelligence make subject matter experts obsolete?

Imagine you want to build an application that helps to identify wine and cheese pairings. Who will perform best? Applications solely based on machine learning, those ones which are based on experts' knowledge only, or a combination of both?

Most of the machine learning algorithms were…

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Added by Andreas Blumauer on August 28, 2017 at 3:00am — 1 Comment

A Standard to build Knowledge Graphs: 12 Facts about SKOS

These days, many organisations have begun to develop their own knowledge graphs. One reason might be to build a solid basis for various machine learning and cognitive computing efforts. For many of those, it remains still unclear where to start. SKOS offers a simple way to start and opens many doors to extend a knowledge graph over time.

1)…

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Added by Andreas Blumauer on August 28, 2017 at 3:00am — No Comments

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