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Faster Innovation and Development with a Full-Stack AI Strategy

The future is here and companies that have incorporated the latest innovations led by AI in their business processes are reaping the rewards. 

A full-stack AI strategy is the way forward and is being adopted by numerous organizations with their eyes on the…

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Added by Ronald van Loon on October 4, 2019 at 10:59pm — No Comments

Introduction and New Open-sourced Tool for Tableau

Hello Everyone, I've just recently joined this site. Firstly, I would like to share with you all a free and open-sourced tool that I have published on github.com for Tableau that reduces the time required to complete a report. It takes the stress out of remembering what calculated fields are used and where across your workbook by giving you an easy to browse Tableau report that can be refreshed anytime…

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Added by Darren Edwin Braynard on October 3, 2019 at 5:00pm — No Comments

Free Book: A Comprehensive Guide to Machine Learning (Berkeley University)

By Soroush Nasiriany, Garrett Thomas, William Wang, Alex Yang. Department of Electrical Engineering and Computer Sciences, University of California, Berkeley. Dated June 24, 2019. This is not the same book as The Math of Machine Learning, also published by the same department at Berkeley, in 2018, and also authored by Garret…

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Added by Capri Granville on October 3, 2019 at 8:30am — No Comments

Welcome to Entanglish, a Python library for calculating quantum entanglement

"Speak Quantum Friend and Enter"

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Today, I uploaded to github my new software library called Entanglish (open source, under BSD license). Entanglish is a Python toolbox for calculations related to quantum entanglement (including squashed entanglement). Available at …
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Added by Robert R. Tucci on October 3, 2019 at 9:30am — No Comments

How Web scraping and Big Data Analytics can be used to impact the Media and Entertainment industry

Every industry in the world is moving towards data-driven decision making, then one of the most popular and …

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Added by Sandra Moraes on September 26, 2019 at 6:56pm — No Comments

Top 6 Data Science Use Cases in Design

Nowadays, industries are privileged by the opportunity to apply data science to reach new heights in their efficiency, productiveness, and overall success. The range of these opportunities is pervasive starting with advanced calculations for the business to customer service…

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Added by Igor Bobriakov on October 3, 2019 at 3:17am — No Comments

Design Principles for Big Data Performance

The evolution of the technologies in Big Data in the last 20 years has presented a history of battles with growing data volume. The challenge of big data has not been solved yet, and the effort will certainly continue, with the data volume continuing to grow in the coming years. The original relational database system (RDBMS) and the associated OLTP  (Online Transaction Processing) make it so easy to work with data using SQL in all aspects, as long as the data size is small enough to…
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Added by Stephanie Shen on September 29, 2019 at 4:00pm — 1 Comment

Surprising Uses of Synthetic Random Data Sets

I have used synthetic data sets many times for simulation purposes, most recently in my articles Six degrees of Separations between any two Datasets and How to Lie with p-values. Many applications (including the data…

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Added by Vincent Granville on October 2, 2019 at 10:00am — No Comments

Is There a Difference Between Open Data and Public Data?

Yep. And it’s a big one.



There is a general consensus that when we talk about open data we are referring to any piece of data or content that is free to…

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Added by Lewis Wynne-Jones on October 1, 2019 at 9:00am — No Comments

Online Encyclopedia of Statistical Science (Free)

This online book is intended for beginners, college students and professionals confronted with statistical analyses. It is also a refresher for professional statisticians. The book covers over 600 concepts, chosen out of more than 1,500 for their popularity. Entries are listed in alphabetical order, and broken down into 18 parts. In addition to numerous illustrations, we have added 100 topics not covered in our online series Statistical Concepts Explained in Simple English. We also…

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Added by Vincent Granville on June 28, 2019 at 8:00am — 2 Comments

Significance Level vs Confidence level vs Confidence Interval

You may have figured out already that statistics isn't exactly a science. Lots of terms are open to interpretation, and sometimes there are many words that mean the same thing—like "mean" and "average"—or sound like they should mean the same thing, like significance level and confidence level.  

Although they sound very similar, significance level and confidence level are in fact two completely different concepts. Confidence levels and confidence…

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Added by Stephanie Glen on September 30, 2019 at 12:00pm — No Comments

Correlation does not equal causation but How exactly do you determine causation?

 

 

Introduction

 

Co-relation does not equal causation – is a mantra drilled into a Data Scientist from an early age

That’s fine ..

But very few talk of the follow-on question ..

How exactly do you determine causation?

This problem is…

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Added by ajit jaokar on September 30, 2019 at 6:33am — 2 Comments

Is Robotic Process Automation (RPA) Really AI?

Summary:  Based on a McKinsey study we reported that 47% of companies had at least one AI/ML implementation in place.  Looking back at the data and the dominance of RPA as the most widely reported instance makes us think that the number is probably significantly lower.

 

We’ve been trying to get a handle on…

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Added by William Vorhies on March 18, 2019 at 9:00am — 3 Comments

Weekly Digest, September 30

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, follow this link.  

Announcements

  • Earn a Data…
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Added by Vincent Granville on September 29, 2019 at 7:00am — No Comments

Using Machine Learning to Solve Business Problems

This article was written by Soft Media Lab.

It has the following sections.

Contents

  • What is Machine Learning?
  • How to apply machine learning to…
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Added by Andrea Manero-Bastin on September 24, 2019 at 6:30am — No Comments

Artificial Neural Networks in a Nutshell

According to Wikipedia, an ANN is based on a collection of connected units or nodes called artificial neurons, which loosely model the neurons in a biological brain. Each connection, like the synapses in a biological brain, can transmit a signal to other neurons. An artificial neuron that receives a signal then processes it and can signal neurons connected to it.

In ANN implementations, the "signal" at a connection is a real number, and the output of each neuron is computed by some…

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Added by Capri Granville on September 23, 2019 at 4:30am — No Comments

Using a Bathroom Faucet to Teach Neural Network Basic Concepts

They say that the best ideas sometimes come to you while you are in the shower, and this idea of how to explain two important Neural Network concepts – Backpropagation and Stochastic Gradient Descent – actually did come to me as I was trying to set the perfect water temperature for my morning shower.

As I was struggling to adjust the two shower handles – one handle that controlled scolding hot and the other handle that controlled flash freezing – it occurred to me that I was a simple…

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Added by Bill Schmarzo on September 29, 2019 at 4:29am — 4 Comments

Discover How IoT Escalates Vehicle Fleet Safety.

With intense urbanization, the transportation industry works round the clock to suffice the demands of…

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Added by Sanjeev Verma on September 27, 2019 at 1:30am — No Comments

TensorFlow 1.x vs 2.x. – summary of changes

Overview of changes TensorFlow 1.0 vs TensorFlow 2.0

Earlier this year, Google announced TensorFlow 2.0, it is a major leap from the existing TensorFlow 1.0. The key differences are as follows:

 

Ease of use: Many old libraries (example tf.contrib) were removed, and some consolidated. For example, in TensorFlow1.x the model could be made using Contrib, layers, Keras or estimators, so many options for the same task confused many new users.…

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Added by ajit jaokar on September 25, 2019 at 11:30pm — 2 Comments

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