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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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10 Splunk alternatives for log analysis

Splunk may be the most famous way to make sense of mass quantities of log data, but it is far from the only player around

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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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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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HPE plus MapR: Too much Hadoop, not enough cloud

MapR gives HPE superior big data analytics technology and expertise, but not what HPE needs most

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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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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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The data lake is becoming the new data warehouse

Platforms like AWS Lake Formation and Delta Lake point toward a central hub for decision support and AI-driven decision automation

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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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Supervised learning explained

Supervised learning turns labeled training data into a tuned predictive model

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What is TensorFlow? The machine learning library explained

TensorFlow is a Python-friendly open source library for numerical computation that makes machine learning faster and easier

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Hadoop runs out of gas

As big data customers flee complexity and embrace the cloud, the Hadoop vendors are sputtering

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Natural language processing explained

Deep learning has improved machine translation and other NLP tasks by leaps and bounds

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Deep learning explained

Deep neural networks can solve the most challenging problems, but require abundant computing power and massive amounts of data

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4 reasons big data projects fail—and 4 ways to succeed

Nearly all big data projects end up in failure, despite all the mature technology available. Here's how to make big data efforts actually succeed

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Machine learning explained

Able to learn from data, machine learning algorithms can solve problems that are too complex to solve with conventional programming

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Machine learning algorithms explained

Machine learning uses algorithms to turn a data set into a model. Which algorithm works best depends on the problem

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