Machine Learning

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

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Semi-supervised learning explained

Using a machine learning model’s own predictions on unlabeled data to add to the labeled data set sometimes improves accuracy, but not always

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How Qubole addresses Apache Spark challenges

The Qubole Data Platform brings streamlined configuration, auto-scaling, cost management, and performance optimizations to Spark-as-a-service

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IBM Trusted AI toolkits for Python combat AI bias

IBM has released Python toolkits for identifying and mitigating against bias in training data and machine learning models

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Deep learning frameworks: PyTorch vs. TensorFlow

If you actually need a deep learning model, PyTorch and TensorFlow are both good choices

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Machine learning operations don’t belong with cloudops

Giving systems enabled with machine learning to the cloud operations team to manage is not only a mistake, it’s dangerous

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Automated machine learning or AutoML explained

AutoML frameworks and services eliminate the need for skilled data scientists to build machine learning and deep learning models

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AI and your smartphone: How AI is transforming mobile apps

Computerworld’s Ken Mingis and Michael Simon from PCWorld and Macworld talk about how more mobile apps use artificial intelligence and how it improves features.

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How to do real-time analytics across historical and live data

5 in-memory computing platform capabilities that support analytical processing of both data lake data and operational streams

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Unsupervised learning explained

Unsupervised learning is used mainly to discover patterns and detect outliers in data today, but could lead to general-purpose AI tomorrow

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Get started with AI using ML.Net and Model Builder

Microsoft’s .Net machine learning tooling makes it easy to add AI to your code

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5 machine learning tools to ease software development

AI-driven development tools that provide code auto-completion, code vulnerability detection, and even cutting-edge code generation

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The moral side of cloud-based data science

Armed with big data, machine learning, predictive analytics, and unlimited data access, how do we limit the potential for harm?

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Applying devops in data science and machine learning

Having data scientists collaborate with devops and engineers leads to better business outcomes, but understanding their different requirements is key

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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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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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Ease into AI with Microsoft Flow

No-code connectors to Azure Cognitive Services make it easy to get started with artificial intelligence

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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

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Time series analysis with KNIME and Spark

Train and evaluate a simple time series model using a random forest of regression trees and the NYC Yellow taxi data set

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