Remove feature-engineering-tips-for-better-model-performance
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Feature Engineering Tips for Better Model Performance

Dataiku

The art of feature engineering in data science is often a balance between extracting more or new features and removing irrelevant or noisy features — too much or too little, and your model performance degrades, is difficult to interpret, or is overfitted.

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Gen AI without the risks

CIO Business Intelligence

On top of that, Gen AI, and the large language models (LLMs) that power it, are super-computing workloads that devour electricity.Estimates vary, but Dr. Sajjad Moazeni of the University of Washington calculates that training an LLM with 175 billion+ parameters takes a year’s worth of energy for 1,000 US households. Not at all.

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DIY cloud cost management: The strategic case for building your own tools

CIO Business Intelligence

And that’s all before considering the need to fuel new AI initiatives , which can push cloud costs up further. For CIOs who may need to customize their cloud cost information streams or manage a complex cloud estate, do-it-yourself cloud cost management may be the way to go.

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A Practitioner’s Guide to Deep Learning with Ludwig

Domino Data Lab

This blog post considers Ludwig, offering a brief overview of the package and providing tips for practitioners such as when to use Ludwig’s command-line syntax and when to use its Python API. The model definition via a YAML file. The same encoding and decoding models developed for one task can be reused for different tasks.

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Meta-Learning For Better Machine Learning

Rocket-Powered Data Science

In a related post we discussed the Cold Start Problem in Data Science — how do you start to build a model when you have either no training data or no clear choice of model parameters. See the related post for more details about the cold start challenge. This is the meta-learning phase.

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A Guide To The Methods, Benefits & Problems of The Interpretation of Data

datapine

Capable of displaying key performance indicators (KPIs) for both quantitative and qualitative data analyses, they are ideal for making the fast-paced and data-driven market decisions that push today’s industry leaders to sustainable success. Explore our free guide with 5 essential tips for your own data analysis. trillion gigabytes!

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How AI Software is Changing the Future of the Automotive Industry

Smart Data Collective

Automotive OEMs and top automotive software companies can work together to build resilient software development processes with sophisticated AI algorithms that allow them to innovate, meet growing customer needs for infotainment systems, and monetize new business models. The automotive industry is among those investing in AI the most.

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