Data in, intelligence out: Machine learning pipelines demystified

How machine learning pipelines work: Data in, intelligence out #AI #ML #datascience

  • It’s tempting to think of machine learning as a magic black box.
  • If you’re in the business of deriving actionable insights from data through machine learning, it helps for the process not to be a black box.
  • The more you know what’s inside the box, the better you’ll understand every step of the process for how data can be transformed into predictions, and the more powerful your predictions can be.
  • There’s also a pipeline for data as it flows through machine learning solutions.
  • Mastering how that pipeline comes together is a powerful way to know machine learning itself from the inside out.

Data plus algorithms equals machine learning, but how does that all unfold? Let’s lift the lid on the way those pieces fit together, beginning to end
Continue reading “Data in, intelligence out: Machine learning pipelines demystified”

Data in, intelligence out: Machine learning pipelines demystified

Master how to construct a  #machinelearning pipeline

  • It’s tempting to think of machine learning as a magic black box.
  • If you’re in the business of deriving actionable insights from data through machine learning, it helps for the process not to be a black box.
  • The more you know what’s inside the box, the better you’ll understand every step of the process for how data can be transformed into predictions, and the more powerful your predictions can be.
  • There’s also a pipeline for data as it flows through machine learning solutions.
  • Mastering how that pipeline comes together is a powerful way to know machine learning itself from the inside out.

Data plus algorithms equals machine learning, but how does that all unfold? Let’s lift the lid on the way those pieces fit together, beginning to end
Continue reading “Data in, intelligence out: Machine learning pipelines demystified”

AI learns to write its own code by stealing from other programs

#AI has to resort to copying and pasting from StackOverflow too. #programming

  • Ultimately, the approach could allow non-coders to simply describe an idea for a program and let the system build it, says Marc Brockschmidt, one of DeepCoder’s creators at Microsoft Research in Cambridge, UK.
  • DeepCoder uses a technique called program synthesis: creating new programs by piecing together lines of code taken from existing software – just like a programmer might.
  • “It could allow non-coders to simply describe an idea for a program and let the system build it”

    One advantage of letting an AI loose in this way is that it can search more thoroughly and widely than a human coder, so could piece together source code in a way humans may not have thought of.

  • DeepCoder created working programs in fractions of a second, whereas older systems take minutes to trial many different combinations of lines of code before piecing together something that can do the job.
  • Brockschmidt says that future versions could make it very easy to build routine programs that scrape information from websites, or automatically categorise Facebook photos, for example, without human coders having to lift a finger

    “The potential for automation that this kind of technology offers could really signify an enormous [reduction] in the amount of effort it takes to develop code,” says Solar-Lezama.

Software called DeepCoder has solved simple programming challenges by piecing together bits of borrowed code
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The source code behind PhD APIs

Are you developing with the latest technology? Take a look at deep learning source codes:

  • Get sample code for implementing deep learning and natural language processing APIs
  • The code transforms a tedious manual content workflow into a more automated, cost-efficient process.
  • As the API economy enters its second decade, many organizations, from startups to Fortune 500, are recognizing the transformational power of APIs and creating game-changing applications.
  • Savvy developers know where the APIs are and how to put them to use in a way that enables iterative and agile application development.
  • We partnered with ProgrammableWeb to put together a use case to provide the source code and thinking behind NewsMedia Inc.’s (a fictitious company) implementation of deep learning and natural language processing APIs available on Watson Developer Cloud .

As the API economy enters its second decade, many organizations, from startups to Fortune 500, are recognizing the transformational power of APIs and creating game-changing applications. Developers now draw from a nearly infinite palette of internal and public APIs in an effort to outsource almost all of their application functionality.
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TensorKart: self-driving MarioKart with TensorFlow

TensorKart: self-driving MarioKart with TensorFlow

  • After playing way too much MarioKart and writing an emulator plugin in C , I managed to get some decent results.
  • When the plugin is loaded, the emulator checks for several function definitions and errors if any are missing.
  • With this in mind I played more MarioKart to record new training data.
  • Rabbit Hole – writing a mupen64plus input plugin
  • I started by modifying the TensorFlow tutorial for a character recognizer using the MNIST dataset .

Kevin Hughes’ Blog
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Urban Sound Classification using Neural Network

Urban Sound Classification w/ Neural Networks:  #abdsc #BigData #DataScience #MachineLearning

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  • How about teaching computer to classify such sounds automatically into categories!
  • Added by Tim Matteson 0 Comments 0 Likes
  • Earlier blog posts covered classification problems where data can be easily expressed in vector form.
  • In the blog post, we will learn techniques to classify urban sounds into categories using machine learning.

We all got exposed to different sounds every day. Like, the sound of car horns, siren and music etc. How about teaching computer to classify such sounds automa…
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