If you want to learn how to build efficient React applications, this is your book. Ideal for web developers and software engineers who understand how JavaScript, CSS, and HTML work in the browser, this updated edition provides best practices and patterns for writing modern React code. No prior knowledge of React or functional programming is necessary.
Authors Alex Banks and Eve Porcello show you how to create UIs that can deftly display changes without page reloads on large-scale data-driven websites. You’ll also discover how to work with functional programming and the latest ECMAScript features. Once you learn how to build React components with this hands-on guide, you’ll understand just how useful React can be in your organization.
Alex Banks and Eve Porcello are software engineers, authors, and trainers. With their company Moon Highway, they’ve taught JavaScript, Node, React, and GraphQL courses to engineers all over the world. They’ve also created videos and authored training content for O’Reilly, LinkedIn Learning, and egghead.io.
- 本书是统计学习与机器学习领域的权威经典教材，由三位顶尖统计学家撰写，系统涵盖监督学习、无监督学习及高维数据分析等核心主题，为读者提供坚实的统计学理论基础。
- 书中深入讲解Lasso、随机森林、支持向量机、神经网络及Boosting等主流算法的数学原理与实现细节，并特别包含对‘宽’数据（变量数大于样本数）及多重测试校正的前沿方法论述。
- 作者构建了统一的统计学习概念框架，将复杂算法置于同一理论体系下解析，帮助读者理解不同模型间的内在联系与差异，而非孤立地罗列技术，适合希望深入理解算法本质的进阶读者。
- 适合具备线性代数、概率论及微积分坚实基础，且希望深入理解机器学习算法背后统计原理与数学推导的计算机、统计学及相关领域的研究生与科研人员。
- 适合从事数据挖掘、人工智能、金融工程或生物信息学等行业的专业技术人员，他们需要掌握严谨的统计学习方法以解决复杂预测问题，并寻求权威的理论指导。
- 适合对统计学习理论有浓厚兴趣，且能够接受高难度数学推导的读者，本书不适用于寻求快速上手代码实现或零基础入门的初学者，需读者具备较强的数学抽象思维能力。
- 鉴于中文版翻译质量普遍受到读者诟病，存在大量术语误译与表述不通顺的问题，强烈建议读者直接阅读英文原版，或对照英文原版进行辅助学习，以避免因翻译错误导致理解偏差。
- 本书数学难度极高，阅读前需确保熟练掌握矩阵分解、凸优化及概率统计等前置知识，建议配合斯坦福大学官网提供的免费电子版及习题解答手册，以辅助攻克艰深的理论推导部分。
- 不要试图逐字逐句死磕所有数学证明，应重点把握算法的核心思想、适用场景及局限性，对于过于复杂的推导可暂时跳过，先建立宏观的理论框架，再根据需要深入细节。
- 读者普遍认可本书在统计学习与机器学习领域的经典地位与学术价值，认为其内容严谨、覆盖面广，是深入理解算法原理不可或缺的参考书，但同时也一致指出其数学门槛极高，不适合初学者。
- 绝大多数读者强烈批评中文版翻译质量低下，认为其严重损害了原书的逻辑连贯性与可读性，甚至出现专业术语错译，因此强烈建议读者放弃中文版，直接阅读英文原版以获取最佳学习体验。
- 读者反馈本书虽难度极大，但一旦克服数学障碍，将极大提升对机器学习本质的理解，许多读者将其作为博士资格考试或深入研究的重要参考，认为其提供的理论深度远超普通应用类教程。
本导读基于书籍简介、目录、原文摘录、短评和书评生成，不等同于全文精读。
- "LAR uses least squares directions in the active set of variables. Lasso uses least square directions; if a variable crosses zero, it is removed from the active set. Boosting uses non-negative least squares directions in the active set."
- "the bias of the 1-nearest-neighbor estimate is often low, but the variance is high."
- "Monte Carlo is an extremely bad method; it should be used only when all alternative methods are worse"
- "有一个关于Metropolis算法的故事，非常流行：一晚，Edward、Metropolis和Marshall在派对上讨论这个问题，在鸡尾酒餐巾纸上写出了这个闻名的算法。他们最终的论文之所以写上妻子的名字，是为了安抚被整晚的技术性讨论所烦扰的女人Arianna和Augusta"
- "Bagging; or Bootstrap AGGregatING, is an extension of bootstrapping to classification and regression problems. The main idea is to sample with replacement from the training data so that we now have B training data sets, each having n′≤n observations. The machine-learning algorithm is trained on each"
- "还是觉得对这本书相见恨晚，研一写那么都web app有毛用啊，就应该踏踏实实的多读书啊~ 还好去实习了，还好发现原来啥都不知道，还好坚持把这本书啃完了，虽然理解的较为粗陋，要不要去读个博呢......真苦恼~"
- "Both k-nearest neighbors and least squares end up approximating conditional expectatios by averages."
- "However, with a 0 − 1 outcome, this computation simplifies. We order the predictor classes according to the proportion falling in outcome class 1. Then we split this predictor as if it were an ordered predictor."
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