Aurélien Géron《动手实践Scikit-Learn与TensorFlow机器学习》原版PDF

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《动手学习机器学习:用Scikit-Learn与TensorFlow实战》是一本由 Aurélien Géron 所著的专业书籍,专为希望深入理解和实践机器学习的读者设计。这本书并非早期预览版,而是正式出版的完整教程,旨在提供概念、工具和技术,帮助读者构建智能系统。 该书主要围绕Scikit-Learn和TensorFlow这两个在数据科学领域广泛使用的Python库展开,Scikit-Learn是一个功能丰富的机器学习库,而TensorFlow则是谷歌开源的深度学习框架。通过本书,读者将学到如何运用这些强大的工具进行实际操作,包括但不限于数据预处理、模型选择、特征工程、监督学习、无监督学习、深度学习、神经网络等核心概念。 作者Aurélien Géron以其清晰易懂的风格,结合丰富的示例和实战项目,引导读者逐步掌握从基础到进阶的机器学习技术。书中不仅涵盖了理论知识,还提供了大量代码实现,便于读者通过实践来巩固理解。此外,书中还讨论了如何评估模型性能,以及如何处理常见问题和挑战,如过拟合和欠拟合。 对于那些希望在人工智能和机器学习领域提升技能的开发者、研究人员和学生来说,《动手学习机器学习:用Scikit-Learn与TensorFlow实战》是一本不可或缺的参考书。无论你是初学者还是有经验的从业者,都能从中找到适合自己的学习路径和实战项目,以适应不断发展的数据科学和技术环境。 本书的版权信息表明,它享有2017年Aurélien Géron的全部权利,并且是在美国印刷。O'Reilly Media出版社负责发行,提供纸质版和在线版本供不同需求的读者选择。此外,还列出了编辑、生产编辑、校对人员、索引员和设计师等参与书稿制作的专业人士,保证了内容的质量和专业性。 《动手学习机器学习:用Scikit-Learn与TensorFlow实战》是一本实用的指南,适合希望通过实践掌握机器学习技术的专业人士和爱好者使用,是他们提升技能、跟进行业发展的重要资源。
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When most people hear “Machine Learning,” they picture a robot: a dependable butler or a deadly Terminator depending on who you ask. But Machine Learning is not just a futuristic fantasy, it’s already here. In fact, it has been around for decades in some specialized applications, such as Optical Character Recognition (OCR). But the first ML application that really became mainstream, improving the lives of hundreds of millions of people, took over the world back in the 1990s: it was the spam filter. Not exactly a self-aware Skynet, but it does technically qualify as Machine Learning (it has actually learned so well that you seldom need to flag an email as spam anymore). It was followed by hundreds of ML applications that now quietly power hundreds of products and features that you use regularly, from better recommendations to voice search. Where does Machine Learning start and where does it end? What exactly does it mean for a machine to learn something? If I download a copy of Wikipedia, has my computer really “learned” something? Is it suddenly smarter? In this chapter we will start by clarifying what Machine Learning is and why you may want to use it. Then, before we set out to explore the Machine Learning continent, we will take a look at the map and learn about the main regions and the most notable landmarks: supervised versus unsupervised learning, online versus batch learning, instance-based versus model-based learning. Then we will look at the workflow of a typical ML project, discuss the main challenges you may face, and cover how to evaluate and fine-tune a Machine Learning system. This chapter introduces a lot of fundamental concepts (and jargon) that every data scientist should know by heart. It will be a high-level overview (the only chapter without much code), all rather simple, but you should make sure everything is crystal-clear to you before continuing to the rest of the book. So grab a coffee and let’s get started!