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ALBERT Model for Self-Supervised Learning

Analytics Vidhya

Later in 2019, the researchers proposed the ALBERT (“A Lite BERT”) model for self-supervised learning of language representations, which shares the same architectural backbone as BERT. The post ALBERT Model for Self-Supervised Learning appeared first on Analytics Vidhya. The key […].

Modeling 338
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Regime Shift Models – A Fascinating Use Case of Time Series Modeling

Analytics Vidhya

She is one of the eminent speakers at DataHack Summit 2019, where she will be talking about. The post Regime Shift Models – A Fascinating Use Case of Time Series Modeling appeared first on Analytics Vidhya. This article is written by Sonam Srivastava.

Modeling 250
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What is SCOR? A model to improve supply chain management

CIO Business Intelligence

Thats where the SCOR model comes in. What is the SCOR model? The SCOR model is designed to evaluate your supply chain for effectiveness and efficiency of sales and operational planning (S&OP). What is the main focus of the SCOR model? model to further address the growing need for digitization of supply chains.

Modeling 104
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Highlights from the Strata Data Conference in London 2019

O'Reilly on Data

Chris Taggart explains the benefits of white box data and outlines the structural shifts that are moving the data world toward this model. Watch " Privacy, identity, and autonomy in the age of big data and AI.". --> Continue reading Highlights from the Strata Data Conference in London 2019. Watch " The enterprise data cloud.".

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Proposals for model vulnerability and security

O'Reilly on Data

Apply fair and private models, white-hat and forensic model debugging, and common sense to protect machine learning models from malicious actors. Like many others, I’ve known for some time that machine learning models themselves could pose security risks. This is like a denial-of-service (DOS) attack on your model itself.

Modeling 278
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Why you should care about debugging machine learning models

O'Reilly on Data

Not least is the broadening realization that ML models can fail. And that’s why model debugging, the art and science of understanding and fixing problems in ML models, is so critical to the future of ML. Because all ML models make mistakes, everyone who cares about ML should also care about model debugging. [1]

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AI adoption in the enterprise 2020

O'Reilly on Data

The new survey, which ran for a few weeks in December 2019, generated an enthusiastic 1,388 responses. This year, about 15% of respondent organizations are not doing anything with AI, down ~20% from our 2019 survey. It seems as if the experimental AI projects of 2019 have borne fruit. But what kind? Bottlenecks to AI adoption.