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BigData News Thursday, February 15

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BigData News TLDR / Table of Contents

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Cartoon: Machine Learning Problems in 2118

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  • For Valentine’s day, new KDnuggets cartoon looks at some problems Machine Learning can face in 2118.
  • For Valentine’s day, new KDnuggets cartoon looks at some problems Machine Learning can face in 2118.
  • This cartoon was ably drawn by Jon Carter Here are other KDnuggets Valentine’s Day CartoonsSee also other recent KDnuggets Cartoons:

[/vc_column_text][vc_column_text el_class=”topfeed-tags”]Tags: new KDnuggets cartoon, problems Machine, , , [/vc_column_text][/vc_column][vc_column width=”1/2″][vc_separator][vc_column_text el_class=”topfeed-tweet”]

[/vc_column_text][vc_column_text el_class=”topfeed-embedly”]Cartoon: Machine Learning Problems in 2118[/vc_column_text][/vc_column][/vc_row][vc_row el_id=”Machine-Learning-basics-for-a-newbie”][vc_column width=”1/2″][vc_separator][vc_column_text]

Machine Learning basics for a newbie

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  • Here is how I started explaining to these people: – – Machine Learning refers to the techniques involved in dealing with vast data in the most intelligent fashion (by developing algorithms) to derive actionable insights.
  • Machine learning is a set of techniques, which helpin dealing with vast data in the most intelligent fashion (by developing algorithms or set of logical rules) to derive actionable insights (delivering search for users in this case).
  • Also any statistical modelling assumes a number of distributions while machine learning algorithms are generally agnostic of the distribution of all attributes.
  • Also Read: Getting Smart with Machine Learning Ada Boost and Gradient Boost – – Predictive model as the name suggests is used to predict the future outcome based on the historical data.
  • Example of algorithm used here is:K- means Clustering Algorithm – – It is an example of machine learning where the machine is trained to take specific decisions based on the business requirement with the sole motto to maximize efficiency (performance).

[/vc_column_text][vc_column_text el_class=”topfeed-tags”]Tags: machine learning, , , , [/vc_column_text][/vc_column][vc_column width=”1/2″][vc_separator][vc_column_text el_class=”topfeed-tweet”]

[/vc_column_text][vc_column_text el_class=”topfeed-embedly”]Machine Learning Basics For A Newbie | Machine Learning Applications[/vc_column_text][/vc_column][/vc_row][vc_row el_id=”Spark-Deep-Learning-Distributed-Deep-Neural-Network-Training-with-SparkNet”][vc_column width=”1/2″][vc_separator][vc_column_text]

Spark + Deep Learning: Distributed Deep Neural Network Training with SparkNet

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  • But what about the possibility of leveraging the structure of existing batch processing distributed frameworks for training these networks?Distributed data processing frameworks such as Hadoop or Spark have enjoyed widespread adoption and success over the past decade.
  • The ubiquity of Apache Spark implementations in the wild could provide an ideal vehicle for the mass training of deep neural networks, if such a framework could, indeed, be leveraged.And this is where we introduce SparkNet, what its developers at UC Berkeley’s AMPLab describe, quite simply, as implementing a scalable,…
  • Along with the core concept of a scalable, distributed deep neural network training algorithm, SparkNet also includes an interface for reading from Spark’s data abstraction, known as the Resilient Distributed Dataset (RDD), a Scala interface for interacting with the Caffe deep learning framework (which is written in C++), and a…
  • A framework such as Spark would allow cleaning, preprocessing, and other data-related tasks to be handled via the single system, and datasets could be kept in memory for the entire processing pipeline, eliminating expensive disk writes.The hardware requirements for SparkNet are minimal.
  • The developers also stress that their goal is not to outperform existing computational frameworks, but instead to provide an entirely different paradigm, which just happens to be implemented on top of a popular batch framework such as Apache Spark, and which performs nearly as well as specialized deep learning frameworks,…

[/vc_column_text][vc_column_text el_class=”topfeed-tags”]Tags: deep neural networks, deep learning, Apache Spark implementations, Spark worker nodes, Caffe deep learning[/vc_column_text][/vc_column][vc_column width=”1/2″][vc_separator][vc_column_text el_class=”topfeed-tweet”]

[/vc_column_text][vc_column_text el_class=”topfeed-embedly”]Spark + Deep Learning: Distributed Deep Neural Network Training with SparkNet[/vc_column_text][/vc_column][/vc_row][vc_row el_id=”Big-Companies-Are-Embracing-Analytics-But-Most-Still-Don-t-Have-a-Data-Driven-Culture”][vc_column width=”1/2″][vc_separator][vc_column_text]

Big Companies Are Embracing Analytics, But Most Still Don’t Have a Data-Driven Culture

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  • Almost four in five respondents said they feared disruption or displacement from firms like those in the fintech sector or firms specializing in big data.
  • There is both a stronger feeling that big data and AI projects deliver value and a greater concern that established firms will be disrupted by startups.
  • The actual respondents are changing somewhat from the first surveys:It has always involved a high proportion of C-level executives responsible for data, but this year chief data officersare 56% of the respondents, up from 32% last year.
  • Only 12% of firms in the 2012 survey had even appointed a chief data officer.
  • Almost four in five respondents said they feared disruption or displacement from firms like those in the fintech sector or firms specializing in big data.

[/vc_column_text][vc_column_text el_class=”topfeed-tags”]Tags: big data, chief data officer, AI, firms, [/vc_column_text][/vc_column][vc_column width=”1/2″][vc_separator][vc_column_text el_class=”topfeed-tweet”]

[/vc_column_text][vc_column_text el_class=”topfeed-embedly”]Big Companies Are Embracing Analytics, But Most Still Don’t Have a Data-Driven Culture[/vc_column_text][/vc_column][/vc_row][vc_row el_id=”Big-Data-Combined-With-Machine-Learning-Helps-Businesses-Make-Much-Smarter-Decisions”][vc_column width=”1/2″][vc_separator][vc_column_text]

Big Data Combined With Machine Learning Helps Businesses Make Much Smarter Decisions

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  • Today, the importance of machine learning and big data to businesses cannot be overemphasized; both are revolutionizing business operations and consistently providing lots of new opportunities.
  • Here are some four ways by which combining big data with machine learning has helped improve business intelligence, and some takeaways for business owners.
  • Google, for example, uses big data to better understand your preferences and combines it with complex (machine learning) algorithms to provide supposedly relevant results for every query you make.
  • Related:Top 10 Best Chatbot Platform Tools to Build Chatbots for Your Business – – After gaining insight into consumer behavior from big data, you’ll want to use machine learning to develop generalizations and thus make predictions regarding various business issues.
  • This article revealed some four ways by which a combination of machine learning and big data, if applied, can be a fillip to business intelligence.

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[/vc_column_text][vc_column_text el_class=”topfeed-embedly”]Big Data Combined With Machine Learning Helps Businesses Make Much Smarter Decisions[/vc_column_text][/vc_column][/vc_row][vc_row el_id=”China-s-AI-startups-scored-more-funding-than-America-s-last-year”][vc_column width=”1/2″][vc_separator][vc_column_text]

China’s AI startups scored more funding than America’s last year

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  • The UK says Russia was behind the huge NotPetya ransomware attack – – – – – In a rare example of directly attributing blame, the Britishgovernmentsays Russia orchestrated themassive cyberattack in 2017.
  • Back story: Last summer a new breed of ransomware, dubbed NotPetya and based on a Windows flaw leaked from the NSA, held… Read more – – – – – – In a rare example of directly attributing blame, the Britishgovernmentsays Russia orchestrated themassive cyberattack in 2017.
  • Back story: Last summer a new breed of ransomware, dubbed NotPetya and based on a Windows flaw leaked from the NSA, held computers around the world hostage.
  • Blaming Russia:According to the Guardian, the UKs foreign office minister for cybersecurity, Lord Ahmad, says that the Russian government, specifically the Russian military, was responsible for the destructive NotPetya cyberattack.
  • Bracing for more: Meanwhile, UK defense secretaryGavin Williamson says, We have entered a new era of warfare, witnessing a destructive and deadly mix of conventional military might and malicious cyberattacks … We must be primed and ready to tackle these stark and intensifying threats.

[/vc_column_text][vc_column_text el_class=”topfeed-tags”]Tags: AI, AI startup investment, enterprise AI offerings, AI startups, AI systems[/vc_column_text][/vc_column][vc_column width=”1/2″][vc_separator][vc_column_text el_class=”topfeed-tweet”]

[/vc_column_text][vc_column_text el_class=”topfeed-embedly”]China’s AI startups scored more funding than America’s last year – MIT Technology Review[/vc_column_text][/vc_column][/vc_row][vc_row el_id=”SQL-vs-NoSQL-Optimal-Uses-Pepe-Kamel-“][vc_column width=”1/2”][vc_separator][vc_column_text]

SQL vs NoSQL — Optimal Uses – Pepe Kamel –

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  • SQL vs NoSQLOptimalUsesSQL is a relational, multi-threaded and multi-user database management system with more than six million installations; used by many large and popular websites, such as Wikipedia, Google (not for searches), Facebook, Twitter, Flickr, and YouTube.
  • NoSQL allows you to distribute large amounts of information; while SQL facilitates the distribution of relational databases.
  • NoSQL allows horizontal scaling without problems – due to its distribution capacity-; while scaling SQL is more complicated.SQL vs. NoSQL ANALOGY:SQL databases resemble automatic transmission in vehicleswhileNoSQL resemblesmanual transmission.
  • Once you change to NoSQL, the user takes charge of a large amount of manual work that in SQL, the system would be responsible for automatically.
  • In most opinions, a relational database can be used in the following areas:Education: to structure information, and provide logical knowledge to the student.Web development: to maintain data hierarchy, as long as the capacity of concurrency, storage and maintenance are not of considerable difficulty and the information is consistent.Small Business: intelligence…

[/vc_column_text][vc_column_text el_class=”topfeed-tags”]Tags: nosql, SQL, NoSQL ANALOGY, NoSQL databases, relational databases[/vc_column_text][/vc_column][vc_column width=”1/2″][vc_separator][vc_column_text el_class=”topfeed-tweet”]

[/vc_column_text][vc_column_text el_class=”topfeed-embedly”]SQL vs NoSQL — Optimal Uses – Pepe Kamel – Medium[/vc_column_text][/vc_column][/vc_row][vc_row el_id=”Why-You-Need-To-Use-Smart-Data-To-Improve-Customer-Experience”][vc_column width=”1/2″][vc_separator][vc_column_text]

Why You Need To Use Smart Data To Improve Customer Experience

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    [/vc_column_text][vc_column_text el_class=”topfeed-tags”]Tags: continuously improvement Datafloq, one-stop source, big data, artificial intelligence, [/vc_column_text][/vc_column][vc_column width=”1/2″][vc_separator][vc_column_text el_class=”topfeed-tweet”]

    [/vc_column_text][vc_column_text el_class=”topfeed-embedly”]Why You Need To Use Smart Data To Improve Customer Experience[/vc_column_text][/vc_column][/vc_row]

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