This book will teach you how to do data science with R: You’ll learn how to get your data into R, get it into the most useful structure, transform it, visualise it and model it. In this book, you will find a practicum of skills for data science. Just as a chemist learns how to clean test tubes and stock a lab, you’ll learn how to clean data and draw plots—and many other things besides. These are the skills that allow data science to happen, and here you will find the best practices for doing each of these things with R. You’ll learn how to use the grammar of graphics, literate programming, and reproducible research to save time. You’ll also learn how to manage cognitive resources to facilitate discoveries when wrangling, visualising, and exploring data.
A. Dosovitskiy, L. Beyer, A. Kolesnikov, D. Weissenborn, X. Zhai, T. Unterthiner, M. Dehghani, M. Minderer, G. Heigold, S. Gelly und 2 andere Autor(en). (2020)cite arxiv:2010.11929Comment: Fine-tuning code and pre-trained models are available at https://github.com/google-research/vision_transformer. ICLR camera-ready version with 2 small modifications: 1) Added a discussion of CLS vs GAP classifier in the appendix, 2) Fixed an error in exaFLOPs computation in Figure 5 and Table 6 (relative performance of models is basically not affected).
jameswilliam. The Client Perspective: Nulogic Business Solutions Reviews and Insights, The Client Perspective: Nulogic Business Solutions Reviews and Insights (200):
4(Dezember 2023)The Client Perspective: Nulogic Business Solutions Reviews and Insights.
Y. Luo, P. Liang, C. Wang, M. Shahin, und J. Zhan. (2021)cite arxiv:2107.07482Comment: 15th ACM/IEEE International Symposium on Empirical Software Engineering and Measurement (ESEM).
E. Walker, B. Collins, und J. Gehin. M&C 2017 - International Conference on Mathematics & Computational Methods Applied to Nuclear Science & Engineering, (April 2017)
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