Machine Learning

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Microsoft’s ML.Net framework adds TensorFlow scoring

This capability enables use of an existing model from Google’s TensorFlow deep learning and machine learning toolkit

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LinkedIn open-sources a tool to run TensorFlow on Hadoop

The Tony project uses Hadoop's native scheduler to run TensorFlow jobs, making fault tolerance and GPU usage easier

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TensorFlow.js puts machine learning in the browser

The WebGL-accelerated library works with the Node.js server-side JavaScript runtime, but isn’t on par with Tensorflow’s Python API

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10 machine learning APIs developers will love

Tap the readymade machine learning models behind these cloud-based APIs to add a stroke of genius to your app

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How artificial intelligence is reshaping the insurance industry

Artificial intelligence is changing the world of insurance, improving underwriting and accessibility while making it more affordable for consumers. In the future, it may be able to make insurance obsolete

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Interview: Matei Zaharia on Spark and machine learning

Zaharia expounds on the reasons Spark has become the big data framework of choice and why he thinks his company’s melding of Spark and machine learning delivers unique value

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Machine learning: When to use each method and technique

What exactly can you do with machine learning? We explain the various methods and techniques available to you

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Kubeflow brings Kubernetes to machine learning workloads

Project works with TensorFlow library, eases ML deployments

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Why gaming AI won’t help make AI work in the real world—but could

The underlying game-theory principles could be applied to the real world, only if the test cases reflected the real world, not artificial fantasy environments

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What is CUDA? Parallel programming for GPUs

You can accelerate deep learning and other compute-intensive apps by taking advantage of CUDA and the parallel processing power of GPUs

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When data becomes a service

The need for data to fuel effective artificial intelligence solutions and how to collect it via model-as-a service and insights-as-a service

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What’s new in TensorFlow machine learning

Google’s TensorFlow 2.0 beta is expected later this year, with a focus on improving performance and correcting mistakes in compatibility and continuity

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Machine learning: How to create a recommendation engine

In this excerpt from the book “Pragmatic AI,” learn how to code recommendation engines based on machine learning in AWS, Azure, and Google Cloud

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Why there are no shortcuts to machine learning

As long as companies understand that good data science takes time in an enterprise, and give these people room to learn and grow, they won’t need shortcuts

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Oracle offers GraphPipe spec for machine learning data transmission

GraphPipe is intended to bring the efficiency of a binary, memory-mapped format while being simple and light on dependencies

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Data is the lifeblood of AI, but how do you collect it?

AI and lots of good data go hand in hand, but it can be a challenge for companies to aggregate it

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Artificial intelligence will close the gap on latency in telecommunications

Artificial intelligence is turning out to be the answer to most of our technological problems

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ML.Net aims to provide machine learning for .Net developers

Microsoft's new machine learning framework promises high-level APIs to make model training and predictions easy, along with strong integration of .Net language features


Data in, intelligence out: Machine learning pipelines demystified

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

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Julia tutorial: Get started with the Julia language

Want the convenience of a dynamic language and the performance of a compiled statically typed language? Try Julia

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