This textbook approaches the essence of machine learning and data science by considering math problems and building Python programs.\nAs the preliminary part, Chapter 1 provides a concise introduction to linear
斯坦福大学《统计学习导论2023Python版|An Introduction to Statistical Learning with Python》中英字幕 3.7万播放 [001]1.1 Opening Remarks.zh_en 18:19 [002]8 Years Later (Second Edition of the Course).zh_en 02:19 [003].Third Edition of the Course I 2023.zh_en 01:49 [004]1.2 Examples and ...
Title: An Introduction to Statistical Learning: with Applications in Python Author(s) Gareth James, Daniela Witten, Trevor Hastie, Robert Tibshirani Publisher: Springer; 1st ed. 2023 edition (September 8, 2023); eBook (July 5, 2023) Hardcover: 619 pages eBook: PDF (613 pages) Language: ...
scMODAL: a general deep learning framework for comprehensive single-cell multi-omics data alignment with feature links Single-cell multi-omics integration is challenged by varied feature relationships and modality-specific limitations. Here, the authors present scMODAL, a deep learning framework that ...
Python, giving tutorials from the ground up, and progressing with more detailed sessions that implement the techniques in each chatper. We also offer the separate and original version of this course called Statistical Learning with R – the chapter lectures are the same, but the lab lectures ...
and unsupervised learning. The second part on inferential data analysis covers linear and logistic regression and regularization. The last part studies machine learning with a focus on support-vector machines and deep learning. Each chapter is based on a dataset, which can be downloaded from the bo...
Each edition contains a lab at the end of each chapter, which demonstrates the chapter’s concepts in either R or Python. The chapters cover the following topics: What is statistical learning? Regression Classification Resampling methods Linear model selection and regularization ...
Module 6 of Math 569: Statistical Learning delves into model evaluation and model selection via hyperparameter choice. It begins with an understanding of Bias-Variance Decomposition, highlighting the trade-off between model simplicity and accuracy. The module then explores model complexity, offering stra...
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This book provides an accessible overview of the field of Statistical Learning, an essential toolset for making sense of the vast and complex data sets that have emerged in fields ranging from biology to finance to marketing to astrophysics in the past twenty years. This...