Machine Learning

Machine Learning | News, how-tos, features, reviews, and videos

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Machine learning algorithms explained

Machine learning uses algorithms to turn a data set into a predictive model. Which algorithm works best depends on the problem

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Review: Kinetica analyzes billions of rows in real time

GPU database is not only hugely scalable, but integrates graph analysis, location intelligence, and machine learning with standard SQL

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Review: Google Cloud AutoML is truly automated machine learning

Google’s AutoML lets you create customized deep learning models without any knowledge of data science or programming

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AWS piles on the machine learning services

Hosted TensorFlow, pay-as-you-go inference serving, reinforcement learning, and automatic data labeling come to the Amazon cloud

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Machine learning lessons: 5 companies share their mistakes

Machine learning can also go terribly wrong, making you regret that enthusiastic rush to adopt

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How to use Knime for data science

Free, open-source Knime allows you to visually assemble data processing “nodes” into machine learning, deep learning, and other analytics workflows

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7 cloud services to ease machine learning

Build, train, tune, and deploy machine learning and deep learning models in these end-to-end machine learning clouds

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Machine learning: How to go from theory to reality

A lack of skilled people continues to stymie the AI revolution. That’s why smart companies invest as much in cultural change as technology adoption—and Google shows how

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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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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

New Kubernetes project works with the TensorFlow library to ease machine learning deployments

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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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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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3 big data platforms look beyond Hadoop

Learn how the Cloudera, Hortonworks, and MapR data platforms are evolving to meet the demands for real-time analytics and machine learning

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