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BigData News Thursday, July 19

[Webinar Q/A Part 1] Big Data Hadoop Administration

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[Webinar Q/A Part 1] Big Data Hadoop Administration

Infographics

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Infographics

Breakingviews – Really Big Data gives China medical AI edge

  • Investors atlast weeks annual RISE technology conference in Hong Kongtalked up a coming healthcare and the hype is as palpable in Silicon Valley.
  • REUTERS/Jason Reed – – – U.S. venture capitalfundspoured over $12 billion into local biotech, pharmaceutical, and medical-device upstarts in the first half of this year, according to data fromPitchbook, on track tosurpass last years recordof $17 billion.
  • Investments inthe countrys life sciences sector doubled to $12 billion last year, according to ChinaBio Consulting, and accelerated to more than $5 billion in the first quarter of2018,led by prolific backers like Qiming Ventures and Sequoia Capital China.
  • Chinas population of 1.4 billion – anda governmentwilling and able to encouragethe sharing of data -giveshealthcare AIcompanies therean advantage.
  • Future giants are waiting in the wings: Shenzhens $6 billion BGI Genomics is already the worlds top sequencer, while biotech upstarts like the Fidelity-backed Innovent Biologics are gearing up for Hong Kong listings.

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Breakingviews – Really Big Data gives China medical AI edge

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Deep Learning Research Review: Natural Language Processing

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Deep Learning Research Review: Natural Language Processing

Emoji Scavenger Hunt by Google Brand Studio

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Emoji Scavenger Hunt by Google Brand Studio | Experiments with Google

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IBM Code

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IBM Code

Kaggle: Your Home for Data Science

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Kaggle: Your Home for Data Science

Top 10 Machine Learning Algorithms

  • Most of them seem to define top as oldest, and thus most used, ignoring modern, efficient algorithms fit for big data, such asindexation, attribution modeling, collaborative filtering, or recommendation engines used by companies such as Amazon, Google, or Facebook.
  • Some of these techniques such as Naive Bayes (variables are almost never uncorrelated),Linear Discriminant Analysis (clusters are almost never separated by hyperplanes), orLinear Regression (numerous model assumptions – including linearity – are almost always violated in real data)have been so abusedthat I would hesitate teaching them.
  • You might have to attend classes taught by real practitioners (people who worked for big data solutions vendors) to learn modern tools that will give you a competitive edge on the job market.
  • An publisher such as O’Reilly, as well as some universities with an applied data science department, provide good education about these state-of-the-art techniques, with case studies.
  • My upcoming book Data Science 2.0will cover much of the topic, and my previous Wiley bookis a good starting point.

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Top 10 Machine Learning Algorithms