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所在平台: Udemy |
课程主页: https://www.udemy.com/course/hyperparameter-optimization-for-machine-learning/
课程评论：没有评论
**课程名称：** 机器学习中的超参数优化 **课程概述：** 本课程旨在教授学员选择最佳超参数以提升机器学习模型性能的多种技术。无论您是因爱好或工作需要训练机器学习模型，渴望在数据科学竞赛中取得优异成绩，还是仅仅想深入了解超参数调优，本课程都将一步步带您掌握相关知识。 课程内容全面，涵盖了几乎所有可用的超参数优化方法，深入探讨了每种方法的原理、优缺点、应用注意事项以及如何在Python中进行实现。 **主要学习内容：** * 超参数的概念及其调优的重要性 * 使用交叉验证和嵌套交叉验证进行优化 * 网格搜索和随机搜索超参数的方法 * 贝叶斯优化 * Tree-structured Parzen estimators (TPE) * SMAC、基于种群的优化 (Population Based Optimization) 及其他 SMBO 算法 * 使用 Hyperopt, Optuna, Scikit-optimize, Keras Turner 等开源库实现这些技术 **课程成果：** 完成本课程后，学员将能够根据具体需求选择合适的超参数优化方法，并熟练运用现有的开源库进行实践。 **课程特色：** * 超过 50 个讲座，约 8 小时的视频内容。 * 所有主题均包含动手实践的 Python 代码示例，可供参考、练习及复用。 **目标学员：** 任何希望提升机器学习模型性能、在数据科学领域取得进步的学习者。
Welcome to Hyperparameter Optimization for Machine Learning. In this course, you will learn multiple techniques to select the best hyperparameters and improve the performance of your machine learning models.If you are regularly training machine learning models as a hobby or for your organization and want to improve the performance of your models, if you are keen to jump up in the leader board of a data science competition, or you simply want to learn more about how to tune hyperparameters of machine learning models, this course will show you how.We'll take you step-by-step through engaging video tutorials and teach you everything you need to know about hyperparameter tuning. Throughout this comprehensive course, we cover almost every available approach to optimize hyperparameters, discussing their rationale, their advantages and shortcomings, the considerations to have when using the technique and their implementation in Python.Specifically, you will learn:What hyperparameters are and why tuning mattersThe use of cross-validation and nested cross-validation for optimizationGrid search and Random search for hyperparametersBayesian OptimizationTree-structured Parzen estimatorsSMAC, Population Based Optimization and other SMBO algorithmsHow to implement these techniques with available open source packages including Hyperopt, Optuna, Scikit-optimize, Keras Turner and others.By the end of the course, you will be able to decide which approach you would like to follow and carry it out with available open-source libraries.This comprehensive machine learning course includes over 50 lectures spanning about 8 hours of video, and ALL topics include hands-on Python code examples which you can use for reference and for practice, and re-use in your own projects.So what are you waiting for? Enroll today, learn how to tune the hyperparameters of your models and build better machine learning models.
