Big Data
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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
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
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
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
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
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
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
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
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
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
Supervised learning explained
Supervised learning turns labeled training data into a tuned predictive model
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
Hadoop runs out of gas
As big data customers flee complexity and embrace the cloud, the Hadoop vendors are sputtering
Natural language processing explained
Deep learning has improved machine translation and other NLP tasks by leaps and bounds
Deep learning explained
Deep neural networks can solve the most challenging problems, but require abundant computing power and massive amounts of data
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
Machine learning explained
Able to learn from data, machine learning algorithms can solve problems that are too complex to solve with conventional programming
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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