深度学习实战:基于Scikit-Learn, Keras与TensorFlow 2nd版

5星 · 超过95%的资源 需积分: 50 203 下载量 177 浏览量 更新于2024-07-16 7 收藏 69.55MB PDF 举报
"《Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow 2nd Edition》是Aurélien Géron所著的一本关于机器学习实践的书籍,涵盖了使用Scikit-Learn、Keras和TensorFlow构建智能系统的概念、工具和技术。这本书旨在帮助读者深入了解并应用这些强大的开源库进行机器学习项目。" 本书第二版于2019年出版,由O'Reilly Media发行。内容包括了从基础到进阶的机器学习概念,如监督学习、无监督学习、模型评估与选择,以及深度学习的核心原理。书中详细介绍了Scikit-Learn库的使用,这是一个广泛使用的Python机器学习框架,包含了各种预处理、建模和评估算法。 Keras部分则讲解了如何利用这个高级神经网络API来快速构建深度学习模型。Keras的易用性和灵活性使得初学者也能高效地进行实验。而TensorFlow部分则深入到底层,探讨了这个强大的计算库,它是实现大规模机器学习和深度学习的基础。 书中的内容不仅限于理论,还包含了大量的实践案例和代码示例,读者可以跟随这些实例一步步学习如何构建和训练模型。此外,作者还讨论了模型优化、超参数调整、数据预处理等关键问题,这些都是提高模型性能的关键步骤。 本书的读者群体主要是对机器学习感兴趣的开发者、数据科学家和研究人员,无论他们是否有深厚的编程背景,都能通过这本书获得宝贵的实践经验。书中还包括了错误跟踪链接,以便读者获取最新的修订和更新信息。 《Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow 2nd Edition》是一本全面的实战指南,它将帮助读者掌握现代机器学习工具,提升解决实际问题的能力,并且随着技术的发展,持续保持知识的更新。
2017-12-23 上传
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!