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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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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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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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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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PyTorch 1.0 accelerates Python machine learning with native code

The PyTorch 1.0 release candidate introduces Torch Script, a Python subset that can be JIT-compiled into C++ or other high-speed code

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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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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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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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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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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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Review: Amazon SageMaker scales deep learning

AWS machine learning service offers easy scalability for training and inference, includes a good set of algorithms, and supports any others you supply

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PyTorch tutorial: Get started with deep learning in Python

Learn how to create a simple neural network, and a more accurate convolutional neural network, with the PyTorch deep learning library

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For developers, the focus is deep learning, multiplatform, and coding skills

Angular, TensorFlow, React, and Electron all have seen large increases in developer activity on GitHub

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What is deep reinforcement learning: The next step in AI and deep learning

Reinforcement learning is well-suited for autonomous decision-making where supervised learning or unsupervised learning techniques alone can’t do the job

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