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BigData News Thursday, July 12

Is Your Marketing Team Using AI and Machine Learning…Yet? – Content Marketing Place

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Is Your Marketing Team Using AI and Machine Learning…Yet? – Content Marketing Place

Using Machine Learning to predict the outcome of a zzuf fuzzing campaign

  • To obtain a prediction of every testcase, we extracted dynamic features using the same procedure and parameters defined in the VDiscover technical report.
  • Then VDiscover will learn to find more bugs just picking the testcases from the same programs it saw during the training.
  • After running 20 independent experiments (e.g. shuffling the training and testing subsets), the average recall scores are: – – As you can see from the results, VDiscover is quite effective detecting testcases that uncover no bugs, but not so much with the interesting ones.
  • If we recall the percentage of testcases found buggy (26%) and robust (74%), we can compute which is the percentage of all the testcases our tool flags as potentially buggy to fuzz using a weighted average: – – which we can visualize here: – – Consequently, by analyzing 16.84% of…
  • Therefore, in terms of our experimental results, we can detect the same amount of buggy testcases 249% faster ($\approx$ 42%/16.84%).

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Using Machine Learning to predict the outcome of a zzuf fuzzing campaign

What machine learning means for software development

  • So, machine learning is an option when you dont know how to write the software, but you can collect the data.
  • Its hard to imagine collecting the data youd need to train a machine learning algorithmbut if you are able to collect data, the program you produce will be better at adapting to different situations and detecting anomalies, particularly if theres a human in the loop.
  • Thinking more systematically, Peter Norvig has argued that machine learning can be used to generate short programs (but not long ones) from training data; to optimize small parts of larger programs, but not the entire program; and possibly to (with the help of humans) be better tutors to beginning programmers….
  • Machine learning is already making its way into other areas of data infrastructure.
  • These are all tasks for which machine learning is well-suited, and were increasingly seeing software like MLFlow used to manage data pipelines.

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What machine learning means for software development

Machine Learning Can Help B2B Firms Learn More About Their Customers

  • Traditional B2B insight activities have involved such limited data as size of companies as measured by revenue, capitalization or employees, and industry type as formally classified by SIC codes.
  • B2B, or the process of marketing and selling product and service offerings to business customers, is experiencing an intensified focuswith the increased availability of new digital data that describes businesses.
  • Traditional B2B insight activities have involved such limited data as size of companies as measured by revenue, capitalization or employees, and industry type as formally classified by SIC codes.
  • By helping B2B companies gather better data on their customers, AI will help them catch up with their B2C peers.
  • EverString Technology considers the diverse sectors of the web that contain descriptive information of businesses (for example, site domains and employee digital footprints) and incorporates input from expert practitioners in the B2B space to help further describe individual businesses.

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Machine Learning Can Help B2B Firms Learn More About Their Customers

Humans in the Loop for Machine Learning

  • Current ML solutions are sophisticated and may be combined to create broader applications, but they lack the real-world knowledge and human experience needed to create valid and acceptable outcomes on their own.
  • An increasing part of the ML solution is human-in-the-loop capabilities where the machine matches a pattern but human input determines its validity and helps to refine the result.
  • Humans contribute by providing knowledge and capabilities that are impossible or inefficient for an ML solution.
  • Human experts have implicit biases from experience; ML can help you identify these biases, but it may also need to be checked for spurious inferences.
  • Examples of human-in-the-loop ML today include Pinterest’s use of automated human evaluation to filter out certain types of images based on crowdsourcing; start-up StitchFix’s systems that train fashion classifier routines using a trained crowd; Google’s use of humans in building intelligent search with ML, Facebook’s image tagging through a combination…

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Humans in the Loop for Machine Learning | Transforming Data with Intelligence

tensorflow/tensorflow

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tensorflow/tensorflow

Design Patterns for Deep Learning Architectures

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Design Patterns for Deep Learning Architectures

Big Data – naughty or nice? – Data Science Central

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Big Data – naughty or nice? – Data Science Central

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Doing good data science – O’Reilly Media

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Doing good data science – O’Reilly Media

Top Big Data Courses

The Ultimate Hands-On Hadoop - Tame your Big Data! (31,889 students enrolled)

By Sundog Education by Frank Kane
  • Design distributed systems that manage "big data" using Hadoop and related technologies.
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Learn more.


Taming Big Data with MapReduce and Hadoop - Hands On! (13,894 students enrolled)

By Sundog Education by Frank Kane
  • Understand how MapReduce can be used to analyze big data sets
  • Write your own MapReduce jobs using Python and MRJob
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  • Analyze social network data using MapReduce
  • Analyze movie ratings data using MapReduce and produce movie recommendations with it.
  • Understand other Hadoop-based technologies, including Hive, Pig, and Spark
  • Understand what Hadoop is for, and how it works

Learn more.