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5 reasons data science belongs in the cloud

Why laptop-hugging data scientists and server-hugging machine learning practitioners should consider letting go

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How to use deep learning to write Shakespeare

LSTM recurrent neural networks can be trained to generate free text. Let’s see how well AI can imitate the Bard

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IBM preps Watson AI services to run on Kubernetes

IBM Watson services arrive in versions that can run on the public cloud or on privately hosted container infrastructure

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Reinforcement learning comes into AI’s mainstream

Developers now have the tools to get started with this revolutionary technology that is poised to become mainstream

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Review: MXNet deep learning shines with Gluon

With the addition of the high-level Gluon API, Apache MXNet rivals TensorFlow and PyTorch for developing deep learning models

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Explainable AI: Peering inside the deep learning black box

Why we must closely examine how deep neural networks make decisions, and how deep neural networks can help

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How the Turing GPU will revolutionize immersive AI

AI, machine learning, and image manipulation have a lot in common, so it’s no surprise that GPUs can do both, which will bring us amazingly immersive apps

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The future is cloudy, with a chance of success

True cloud computing metaphors: not every cloud is a rain cloud, and too much rain is disastrous for the unprepared

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The best open source software for machine learning

InfoWorld’s 2018 Best of Open Source Software Award winners in machine learning and deep learning

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Bossies 2018: The Best of Open Source Software Awards

InfoWorld recognizes the leading open source projects for software development, cloud computing, big data, and machine learning

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How to solve AI’s reproducibility crisis

Society will never fully trust artificial intelligence’s actions unless they can be shown to produce reasonably repeatable results in line with what their developers claim

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Review: Keras sails through deep learning

Keras sequential models make deep neural network modeling about as simple as it can be

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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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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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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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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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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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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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Powering AI: The explosion of new AI hardware accelerators

Specialized hardware platforms are the future of artificial intelligence, machine learning, and deep learning at every tier and for every task in the cloud-to-edge world in which we will live

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PyTorch review: A deep learning framework built for speed

PyTorch 1.0 shines for rapid prototyping with dynamic neural networks, auto-differentiation, deep Python integration, and strong support for GPUs

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