Machine Learning

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Learn PyTorch: The best free online courses and tutorials

Look no further than these excellent free resources to master the development of deep learning models using PyTorch

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The unwavering optimism of Tim O'Reilly

InfoWorld finds the Silicon Valley veteran in an at once optimistic, yet fiercely critical mood, as he talks about the opportunities artificial intelligence could afford society, while also addressing the risks of such technology if...

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Kaggle: Where data scientists learn and compete

By hosting datasets, notebooks, and competitions, Kaggle helps data scientists discover how to build better machine learning models

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How AI is reshaping healthcare during COVID-19 -- and beyond

Before the COVID-19 crisis, AI and machine learning technologies had just begun to move beyond the pilot stage at large healthcare companies -- but that's changing fast.

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MLflow is now a Linux Foundation project

Databricks framework for managing machine learning projects will go to an open governance model

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AI is now a C-suite imperative

Survey from Appen finds that C-level involvement in AI initiatives has jumped significantly

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How PagerDuty helps customer service and IT teams improve responses

PagerDuty uses machine learning to anticipate issues and dramatically accelerate responses, so problems are addressed before they impact customers

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When not to use AIops for cloudops

The excitement around AIops means that it’s sometimes being deployed for the wrong reasons. Here are a few situations where AIops is contraindicated

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Redis 6: A high-speed database, cache, and message broker

Redis is a powerful blend of speed, resilience, scalability, and flexibility, and Redis Enterprise takes it even further

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3 ways to apply agile to data science and dataops

Take an agile approach to dashboards, machine learning models, cleansing data sources, and data governance

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Working with the Azure Kinect Developer Kit

Building applications on top of the Kinect depth sensor

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Data science during COVID-19: Some reassembly required

Most likely, the assumptions behind your data science model or the patterns in your data did not survive the coronavirus pandemic. Here’s how to address the challenges of model drift

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Quantum AI is still years from enterprise prime time

Quantum computing’s greatest potential for widespread adoption during this decade is in artificial intelligence

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Julia vs. Python: Which is best for data science?

Python has turned into a data science and machine learning mainstay, while Julia was built from the ground up to do the job

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Apache Spark 3.0 adds Nvidia GPU support for machine learning

The next major release of the in-memory data processing framework will support GPU-accelerated functions courtesy of Nvidia RAPIDS

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How data analysis, AI, and IoT will shape the post-pandemic ‘new normal’

Only a common public health infrastructure of AI, cloud computing, streaming, and the Internet of Things can marshal our data against pandemics

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6 Python libraries for parallel processing

Want to distribute that heavy Python workload across multiple CPUs or a compute cluster? These frameworks can make it happen

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Spell machine learning platform goes on-prem

An end-to-end machine learning platform designed for ease of use, Spell now offers incarnations for both public cloud and data center deployment

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How AIOps improves application monitoring

Devops and site reliability engineers are vital to keep applications functioning. AIOps boosts effectiveness another notch

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How to move data science into production

With new Integrated Deployment extensions, data scientists can capture entire KNIME workflows for automatic deployment to production or reuse

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