安娜·加瓦尔达，法国当红女作家，1970年生于巴黎，29岁时她的第一部短篇小说集《我希望有人在什么地方等我》（中译本已由漓江出版社出版）创下了100多万册的销售佳绩，使她从一个平凡的中学教师一跃成为法国光芒四射的明星作家，再次把灰姑娘变公主的童话变成了现实。出书前她在文学界没有任何关系，也不在巴黎生活，却凭借一部短篇小说集征服了千千万万的法国读者。

安娜·加瓦尔达，法国当红女作家，1970年生于巴黎，29岁时她的第一部短篇小说集《我希望有人在什么地方等我》（中译本已由漓江出版社出版）创下了100多万册的销售佳绩，使她从一个平凡的中学教师一跃成为法国光芒四射的明星作家，再次把灰姑娘变公主的童话变成了现实。出书前她在文学界没有任何关系，也不在巴黎生活，却凭借一部短篇小说集征服了千千万万的法国读者。

Ensemble modeling is an approach used to improve the performance of machine learning models. It combines two or more similar or dissimilar machine learning algorithms to deliver superior intellectual powers. This book will help you to implement popular machine learning algorithms to cover different paradigms of ensemble machine learning such as boosting, bagging, and stacking.The Ensemble Machine Learning Cookbook will start by getting you acquainted with the basics of ensemble techniques and exploratory data analysis. You'll then learn to implement tasks related to statistical and machine learning algorithms to understand the ensemble of multiple heterogeneous algorithms. It will also ensure that you don't miss out on key topics, such as like resampling methods. As you progress, you’ll get a better understanding of bagging, boosting, stacking, and working with the Random Forest algorithm using real-world examples. The book will highlight how these ensemble methods use multiple models to improve machine learning results, as compared to a single model. In the concluding chapters, you'll delve into advanced ensemble models using neural networks, natural language processing, and more. You’ll also be able to implement models such as fraud detection, text categorization, and sentiment analysis.By the end of this book, you'll be able to harness ensemble techniques and the working mechanisms of machine learning algorithms to build intelligent models using individual recipes.

An up-to-date, self-contained introduction to a state-of-the-art machine learning approach, Ensemble Methods: Foundations and Algorithms shows how these accurate methods are used in real-world tasks. It gives you the necessary groundwork to carry out further research in this evolving field. After presenting background and terminology, the book covers the main algorithms and th...(展开全部)

An up-to-date, self-contained introduction to a state-of-the-art machine learning approach, Ensemble Methods: Foundations and Algorithms shows how these accurate methods are used in real-world tasks. It gives you the necessary groundwork to carry out further research in this evolving field. After presenting background and terminology, the book covers the main algorithms and th...(展开全部)

罗兰·巴尔特，法国著名结构主义文学理论家与文化评论家。其一生经历可以大致划分为三个阶段：媒体文化评论期(1947-1962)、高等研究院教学期(1962-1976)，以及法兰西学院讲座教授期(1976-1980)。他和存在主义大师萨特在第二次世界大战后法国文学思想界前后辉映，并被公认为蒙田以来法国最杰出的散文大家之一。 罗兰·巴尔特在法国开创了研究社会、历史、文化、文学深层意义的结构主义和符号学方法，发表了大量分析文章和专著，其丰富的符号学研究成果具有划时代的重要性。巴尔特的符号学理论，从崭新的角度，以敏锐的目光，剖析了时装、照片、电影、广告、叙事、汽车、烹饪等各种文化现象的“记号体系”，从而深刻地改变了人们观察和认识世界及历史的方式。晚期巴尔特对当代西方文化和文学的思考进一步深化，超越了前期结构主义立场，朝向有关意义基础、下意识心理、文学...(展开全部)