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Data Science Central Thursday Digest, April 18

Here is our selection of featured articles and resources posted since Monday:

Resources

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Added by Vincent Granville on April 18, 2019 at 12:00pm — No Comments

Reinforcement Learning Explained: Overview, Comparisons and Applications in Business

Imagine you’re completing a mission in a computer game. Maybe you’re going through a military depot to find a secret weapon. You get points for the right actions (killing an enemy) and lose them for the wrong ones (falling into a pit or getting hit). If you’re playing on high difficulty, you might not conclude this task in just one attempt. Try after try, you learn which consecutive actions are needed to get out of a location safe, armed, and equipped with bonuses like extra health points or…

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Added by Kateryna Lytvynova on April 18, 2019 at 2:00am — No Comments

What is the Difference Between AI and Machine Learning

Artificial Intelligence and Machine Learning have empowered our lives to a large extent. The number of advancements made in this space has revolutionized our society and continue making society a better place to live in.

In terms of perception, both Artificial Intelligence and Machine Learning are often used in the same context which leads to confusion. AI is the concept in which machine makes smart decisions whereas Machine Learning is a sub-field of AI…

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Added by Divya Singh on April 17, 2019 at 9:00pm — No Comments

34 Great Articles and Tutorials on Clustering

This resource is part of a series on specific topics related to data science: regression, clustering, neural networks, deep learning, decision trees, ensembles, correlation, Python, R, Tensorflow, SVM, data reduction, feature selection, experimental design, cross-validation, model fitting, and many more. To keep receiving these articles, sign up on…

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Added by Vincent Granville on April 17, 2019 at 8:30am — No Comments

Free Python Data Science coding Book series

In this post, I explain

  1. How you can participate further in the free book series which we are launching based on the early experiences and
  2. Useful resources we recommend based on our experience for learning coding for Data Science (using Python – tensorflow and keras)

 

To provide some context, I posted…

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Added by ajit jaokar on April 17, 2019 at 8:06am — No Comments

“Please, explain.” Interpretability of black-box machine learning models

In February 2019 Polish government added an amendment to a banking law that gives a customer a right to receive an explanation in case of a negative credit decision. It’s one of the direct consequences of implementing GDPR in EU. This means that a bank needs to be able to explain why the loan wasn’t granted if the decision process was automatic.

In October 2018 world headlines reported about …

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Added by Michał Frącek on April 17, 2019 at 7:30am — No Comments

Maximizing Sales with Market Basket Analysis

Sales data analyses can provide a wealth of insights for any business but rarely is it made available to the public. In 2018, however, a retail chain provided Black Friday sales data on Kaggle as part of a Kaggle competition. Although the store and product lines are…

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Added by Ayumi Owada on April 17, 2019 at 6:55am — No Comments

Data Science vs. Decision Science [Infographic]

Data science has become a widely used term and a buzzword as well. It is a broad field representing a combination of multiple disciplines. However, there are adjacent areas that deserve proper attention and should not be confused with data science.…

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Added by Igor Bobriakov on April 17, 2019 at 1:30am — No Comments

Understanding Greedy Classifiers with a Real-World Example

The diverse fields in which machine learning has proven its worth is nothing short of amazing. At the heart of machine learning are the various algorithms it employs to classify data and predict outcomes. This article highlights two greedy classifiers that, albeit simple, can be extremely powerful in their own right.

 

This article is an excerpt from the book…

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Added by Packt Publishing on April 16, 2019 at 11:37pm — No Comments

How To Mimic Evolution For Machine Learning Tasks

Experiments

In his book, The Master Algorithm, Pedro Domingos imagines the following experiments:  

Take a building, extremely wellbuilt for two purposes: Nothing can enter and most importantly nothing can…

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Added by Paul Pinard on April 16, 2019 at 6:00am — No Comments

Real Time Analytics and Stream Processing

   

In many business scenarios it is no longer desirable to wait hours, days or weeks for the results of analytic processes. Psychologically, people expect real-time or near real-time responses from the systems they interact with. Real-time analytics is closely tied to infrastructure issues and recent move to technologies like in-memory databases is beginning to make ‘real-time’ look achievable in the business world and not just in the computer science laboratory.…

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Added by Ajit Singh on April 15, 2019 at 10:16am — No Comments

AI/ML Lessons for Creating a Platform Strategy – Part 2

Summary:  McKinsey says platform companies will represent 30% of global business revenue by next year (2020).  In Part 1 of this article we started to lay out some important lessons learned and examples for you to follow.  Here are the rest.

 

McKinsey says platform companies will represent 30% of global…

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Added by William Vorhies on April 15, 2019 at 7:43am — No Comments

Why Data Science Development Process is like Playing Game Boy® Final Fantasy

Describing the Data Science analytics development process has always been a struggle for me.  The corporate world is full of linear processes.  Major monolithic enterprise applications – ERP, MRP, SFA, CRM systems – have been architected to reduce operational complexity via a series of interlocking, heavily-engineered (and re-engineered) linear processes (though I suspect that anyone who has survived an SAP ERP implementation would take issue with my “reduce…

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Added by Bill Schmarzo on April 15, 2019 at 5:17am — No Comments

Data Science Central Weekly Digest, April 15

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…

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Added by Vincent Granville on April 14, 2019 at 7:00pm — No Comments

Data analysis and visualization in Perl

Guest blog by Stephan Loyd.

Hello everybody, this is my first post here, so forgive me if I screw it up.

Let me firstly introduce background of my work. Several years ago I landed onto a Perl job. It also involves some other languages like Python and R, but it was mainly Perl, until last year focus of my role switched and I still do some Perl but much less since then. I was a little…

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Added by Capri Granville on April 14, 2019 at 6:30pm — No Comments

New Stock Trading and Lottery Game Rooted in Deep Math

I describe here the ultimate number guessing game, played with real money. It is a new trading and gaming system, based on state-of-the-art mathematical engineering, robust architecture, and patent-pending technology. It offers an alternative to the stock market and traditional gaming. This system is also far more transparent than the stock market, and can not be manipulated, as formulas to win the biggest returns (with real money) are made public. Also, it simulates a neutral,…

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Added by Vincent Granville on April 14, 2019 at 1:00pm — 5 Comments

Design Obstacles that Contribute to Job Security

I often encounter blogs highlighting the amount of effort needed to obtain high-quality data for analysis.  Many aspects of this effort seem reasonable to me: there might be cut-and-pasting, merging, parsing, restructuring, extractions, conversions, concatenations, and conditionals.  There might even be coding embedded in the file containing loops, more sophisticated conditionals, and detailed algorithmic processes.  All of these are relatively minor challenges that can be overcome…

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Added by Don Philip Faithful on April 13, 2019 at 7:00am — No Comments

AI-Generated Rap Songs

I often tell my younger coworkers that the most boring way to start a blog post is, “This post is about …” — unless of course you rap it!

 

Yo!

This post is about generating free text

with a deep learning network

particularly it is aboutBrick X6,

Phey, cabe,

make you feel soom the way (I smoke…

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Added by Rosaria Silipo on April 12, 2019 at 1:43pm — No Comments

Bayes Theorem in One Picture

Bayes’ Theorem is a way to calculate conditional probability. The formula is very simple to calculate, but it can be challenging to fit the right pieces into the puzzle. The first challenge comes from defining your event (A) and test (B); The second challenge is rephrasing your question so that you can work backwards: turning P(A|B) into P(B|A). The following image shows a…

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Added by Stephanie Glen on April 12, 2019 at 6:30am — No Comments

Real-World Data Science Challenge: When Is “Good Enough” Actually “Good Enough”

Nothing like a devastating catastrophe to bring a bit of reality into the world of IoT and data science.  The recent crashes of the new Boeing 737 Max have been attributed to a single point of failure – a sensor – in the design of the complex navigation system[1]. 

To summarize the situation:

  • Boeing’s new 737 MAX included larger, more efficient engines than the previous generation and as such, the engines were…
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Added by Bill Schmarzo on April 12, 2019 at 4:33am — No Comments

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