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NVIDIA RAPIDS and Open Source ML Acceleration with Chris Deotte and Jean-Francois Puget

Software Engineering Daily

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Mastering Data Science Competitions

This chapter covers strategies for time and resource allocation in data science competitions, highlighting the significance of a solid testing framework and baseline models. It also discusses the evolving role of large language models and the challenges posed by synthetic data, while advocating for hands-on parameter tuning to deepen understanding of model optimization.

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