# Hands-on Machine Learning with Scikit-Learn, Keras, and TensorFlow, 2nd Edition : Concepts, Tools, and Techniques to Build Intelligent Systems

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- 规范页面：https://alltool.top/book/7af427085ffd4cf986af1b462debd738
- Markdown 版本：https://alltool.top/book/7af427085ffd4cf986af1b462debd738.md
- 作者：Aurelien Geron
- ISBN：9781492032649
- 出版社：O′Reilly
- 出版日期：2019-10-11
- 语言：english
- 分类：计算机、人工智能(AI)
- 评分：10
- 可用格式：PDF（55.83 MB，856 页）、EPUB（46.64 MB，856 页）
- 更新时间：2026-08-04 12:22:25

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- 文件大小：55.83 MB
- 页数：856 页
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## 内容简介

《机器学习实战 (原书第2版) : 基于Scikit-Learn、Keras和TensorFlow》英文版

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 Aurélien Géron 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