Machine Learning
Machine Learning | News, how-tos, features, reviews, and videos
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
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
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
Deep learning frameworks: PyTorch vs. TensorFlow
If you actually need a deep learning model, PyTorch and TensorFlow are both good choices
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
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.
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
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
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
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
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?
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
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
Ease into AI with Microsoft Flow
No-code connectors to Azure Cognitive Services make it easy to get started with artificial intelligence
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
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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