Hands-On Machine Learning with Scikit-Learn and TensorFlow (Paperback) Concepts, Tools, and Techniques for Building Intelligent Systems

Aurélien Géron



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
  • Apply practical code examples without acquiring excessive machine learning theory or algorithm details



作者Aurélien Géron通過使用具體的例子、最少的理論和兩個可用於生產的Python框架——scikit-learn和TensorFlow,幫助您對建立智能系統的概念和工具有直觀的理解。您將學習一系列技術,從簡單的線性回歸到深度神經網絡。每章都有練習題,幫助您應用所學知識,您只需要具備程式設計經驗就可以開始。

- 探索機器學習領域,特別是神經網絡
- 使用scikit-learn從頭到尾追蹤一個機器學習項目的例子
- 探索多種訓練模型,包括支持向量機、決策樹、隨機森林和集成方法
- 使用TensorFlow庫來構建和訓練神經網絡
- 深入研究神經網絡架構,包括卷積網絡、循環網絡和深度強化學習
- 學習訓練和擴展深度神經網絡的技巧
- 應用實用的程式碼示例,無需過多了解機器學習理論或算法細節。