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Rediscovering Semi-Supervised Learning

Dataiku

Many new machine learning projects start with a minimal amount of sampled data, if any. A potential solution is semi-supervised learning (SSL), which uses both labeled and unlabeled samples to perform prediction.

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Finding a Balance With Semi-Supervised Learning

Dataiku

In this blog post, discover how unlabeled data — specifically in the scope of semi-supervised learning — can be used as a “hidden gem” in a variety of machine learning projects.

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5 Unique Challenges for AI in Cybersecurity

Palo Alto Networks

Because of the lack of labels, most detection approaches use unsupervised learning, such as clustering or anomaly detection, as it doesn’t require any labels. As there’s a scarcity of those experts and doing supervised learning is the golden path for cybersecurity AI, that creates another key challenge to doing AI correctly in this space.

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To Annotate or Not? Predicting Performance Drop Under Domain Shift

Dataiku

Dataiku’s AI Lab is dedicated to contributing to the academic machine learning (ML) community and developing tools to assist everyone on their data journey. Dataiku researchers' interests are very broad, from MLOps, active learning, and semi-supervised learning to AutoML and meta ML.

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The Rise of Unstructured Data

Cloudera

This blog discusses quantifications, types, and implications of data. Deep Learning, a subset of AI algorithms, typically requires large amounts of human annotated data to be useful. Enter the field of learning with limited labeled data. The word “data” is ubiquitous in narratives of the modern world. Quantifications of data.

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Feedback based continuous learning in Conversational AI

Zensar

Continuous learning is built on an idea of learning continuously and adaptively from the environment, retaining knowledge, skills and using these for more complex and different tasks. Continuous learning is a key capability of human beings in an interactive environment. It limits the scope of their applications.

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Cloudera wins Risk Markets Technology Award for Data Management Product of the year

Cloudera

With increased digitization, risk-related data sources are expanding not only in volume and velocity but also in the variety of data types – structured, semi-structured and unstructured. The post Cloudera wins Risk Markets Technology Award for Data Management Product of the year appeared first on Cloudera Blog.