Hands-On Machine Learning with Scikit-Learn and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems
Hands-On Machine Learning with Scikit-Learn and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems
复制标题
DOI：
--
复制
发表时间：
2017-04
期刊：
影响因子：
--
通讯作者：
Aurélien Géron
中科院分区：
文献类型：
--
作者：
Aurélien Géron
通过最近的一系列突破，深度学习推动了整个机器学习领域的发展。现在，即使是对这项技术几乎一无所知的程序员也可以使用简单，高效的工具来实现能够从数据中学习的程序。这本实用的书告诉你如何。通过使用具体的例子，最小的理论和两个生产就绪的Python框架-scikit-learn和TensorFlow-作者Aurelien Geron帮助您直观地理解构建智能系统的概念和工具。您将学习一系列技术，从简单的线性回归开始，逐步发展到深度神经网络。每一章都有练习来帮助你应用你所学的知识，你所需要的只是开始编程的经验。探索机器学习领域，尤其是神经网络使用scikit-learn跟踪示例机器学习项目端到端探索多种训练模型，包括支持向量机、决策树、随机森林和集成方法使用TensorFlow库构建和训练神经网络深入研究神经网络架构，包括卷积网络、递归网络、学习用于训练和扩展深度神经网络的技术应用实际的代码示例，而无需获取过多的机器学习理论或算法细节
Through a series of recent breakthroughs, deep learning has boosted the entire field of machine learning. Now, even programmers who know close to nothing about this technology can use simple, efficient tools to implement programs capable of learning from data. This practical book shows you how. By using concrete examples, minimal theory, and two production-ready Python frameworks-scikit-learn and TensorFlow-author Aurelien Geron helps you gain an intuitive understanding of the concepts and tools for building intelligent systems. You'll learn a range of techniques, starting with simple linear regression and progressing to deep neural networks. With exercises in each chapter to help you apply what you've learned, all you need is programming experience to get started. Explore the machine learning landscape, particularly neural nets Use scikit-learn to track an example machine-learning project end-to-end Explore several training models, including support vector machines, decision trees, random forests, and ensemble methods Use the TensorFlow library to build and train neural nets Dive into neural net architectures, including convolutional nets, recurrent nets, and deep reinforcement learning Learn techniques for training and scaling deep neural nets Apply practical code examples without acquiring excessive machine learning theory or algorithm details