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New DoE Program Drives Demand For Machine Learning Programmers

Smart Data Collective

Machine learning is leading to numerous changes in the energy industry. The Department of Energy recently announced that it is taking steps to accelerate the integration of machine learning technology in energy research and development. Machine learning is already disrupting the global energy industry on a massive scale.

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Adding Common Sense to Machine Learning with TensorFlow Lattice

The Unofficial Google Data Science Blog

On the other hand, sophisticated machine learning models are flexible in their form but not easy to control. Introduction Machine learning models often behave unpredictably, as data scientists would be the first to tell you. A more general approach is to learn a Generalized Additive Model (GAM).

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How Nvidia became a trillion-dollar company

CIO Business Intelligence

It was when Nvidia reported strong results for the three months to April 30, 2023, and forecast that its sales could jump by 50% in the following fiscal quarter, that its stock market valuation soared, catapulting it into the exclusive trillion-dollar club alongside well-known tech giants Alphabet, Amazon, Apple, and Microsoft.

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PODCAST: COVID19 | Redefining Digital Enterprises – Episode 6: The Impact of COVID-19 on Supply Chain Management

bridgei2i

By allowing that, they could have a steady demand forecast based on sensing algorithms and react faster to such events. He has delivered hundreds of millions of dollars of impact to his clients in High-Tech CPG and Manufacturing Industries, particularly in the areas of demand forecasting, inventory and procurement planning. Transcript.

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ML internals: Synthetic Minority Oversampling (SMOTE) Technique

Domino Data Lab

Machine Learning algorithms often need to handle highly-imbalanced datasets. This carries the risk of this modification performing worse than simpler approaches like majority under-sampling. A weighted nearest neighbor algorithm for learning with symbolic features. Machine Learning, 57–78. Chawla et al.

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Financial services firms turn to automated, data-driven processes for new products and services

CIO Business Intelligence

Between the host of regulations introduced in the wake of the 2009 subprime mortgage crisis, the emergence of thousands of fintech startups, and shifting consumer preferences for digital payments banking, financial services companies have had plenty of change to contend with over the past decade.