cloud computing - data center - network servers
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The best machine learning and deep learning libraries

TensorFlow, Spark MLlib, Scikit-learn, PyTorch, MXNet, and Keras shine for building and training machine learning and deep learning models

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TensorFlow 2 review: Easier machine learning

Now more platform than toolkit, TensorFlow has made strides in everything from ease of use to distributed training and deployment


Amazon Neptune review: A scalable graph database for OLTP

Amazon’s graph database service offers ACID properties, immediate consistency, and auto-scaling storage for billions of relationships

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Review: UiPath aces robotic process automation

UiPath Enterprise RPA Platform is a remarkably complete toolkit for process automation that is also remarkably easy to use

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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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AnzoGraph: A graph database for deep analytics

AnzoGraph is a fast, horizontally scalable, OLAP graph database that brings a wealth of analytics capabilities to large graphs

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Review: OmniSci GPU database lifts huge data sets

GPU acceleration of database, rendering, and visualization enables interactive exploration of data sets with billions of rows


Das Keyboard 5Q lights up for developers

High-end keyboard is designed to keep developers and IT pros in the loop with key LEDs that double as notifications

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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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Cloud IDE review: AWS Cloud9 vs. Eclipse Che vs. Eclipse Theia

You don’t have to develop locally to get full IDE support. We compare three great tools that let you edit and debug code from a browser

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