Hands-On Machine Learning with Scikit-Learn, Keras, and Tensorflow: Concepts, Tools, and Techniques to Build Intelligent Systems, 3/e (Paperback) (實戰機器學習：使用 Scikit-Learn、Keras 和 TensorFlow 建構智慧系統的概念、工具與技術（第三版）)

Géron, Aurélien

Hands-On Machine Learning with Scikit-Learn, Keras, and Tensorflow: Concepts, Tools, and Techniques to Build Intelligent Systems, 3/e (Paperback)

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商品描述

Through a recent series of 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 best-selling book uses concrete examples, minimal theory, and production-ready Python frameworks--scikit-learn, Keras, and TensorFlow--to help you gain an intuitive understanding of the concepts and tools for building intelligent systems.

With this updated third edition, author Aurelien Geron explores a range of techniques, starting with simple linear regression and progressing to deep neural networks. Numerous code examples and exercises throughout the book help you apply what you've learned. Programming experience is all you need to get started.

  • Use scikit-learn to track an example machine learning project end to end
  • Explore several models, including support vector machines, decision trees, random forests, and ensemble methods
  • Exploit unsupervised learning techniques such as dimensionality reduction, clustering, and anomaly detection
  • Dive into neural net architectures, including convolutional nets, recurrent nets, generative adversarial networks, and transformers
  • Use TensorFlow and Keras to build and train neural nets for computer vision, natural language processing, generative models, and deep reinforcement learning
  • Train neural nets using multiple GPUs and deploy them at scale using Google's Vertex AI

商品描述(中文翻譯)

通過最近一系列的突破，深度學習已經推動了整個機器學習領域。現在，即使對這項技術幾乎一無所知的程序員也可以使用簡單高效的工具來實現能夠從數據中學習的程序。這本暢銷書使用具體的例子、最少的理論和可用於生產的Python框架--scikit-learn、Keras和TensorFlow--幫助您對概念和構建智能系統的工具有直觀的理解。

在這本更新的第三版中，作者Aurelien Geron探索了一系列技術，從簡單的線性回歸到深度神經網絡。書中的眾多代碼示例和練習幫助您應用所學知識。只需要具備編程經驗，您就可以開始學習。

使用scikit-learn從頭到尾跟踪一個機器學習項目的示例
探索多種模型，包括支持向量機、決策樹、隨機森林和集成方法
利用無監督學習技術，如降維、聚類和異常檢測
深入研究神經網絡架構，包括卷積網絡、循環網絡、生成對抗網絡和轉換器
使用TensorFlow和Keras構建和訓練用於計算機視覺、自然語言處理、生成模型和深度強化學習的神經網絡
使用多個GPU訓練神經網絡，並使用Google的Vertex AI在大規模上部署它們

作者簡介

Aurélien Géron is a Machine Learning consultant. A former Googler, he led YouTube's video classification team from 2013 to 2016. He was also a founder and CTO of Wifirst from 2002 to 2012, a leading Wireless ISP in France, and a founder and CTO of Polyconseil in 2001, a telecom consulting firm.

Before this he worked as an engineer in a variety of domains: finance (JP Morgan and Société Générale), defense (Canada's DOD), and healthcare (blood transfusion). He published a few technical books (on C++, WiFi, and Internet architectures), and was a Computer Science lecturer in a French engineering school.

A few fun facts: he taught his 3 children to count in binary with their fingers (up to 1023), he studied microbiology and evolutionary genetics before going into software engineering, and his parachute didn't open on the 2nd jump.

作者簡介(中文翻譯)

Aurélien Géron是一位機器學習顧問。他曾是Google的員工，從2013年到2016年擔任YouTube的視頻分類團隊負責人。他還是Wifirst的創始人和首席技術官，該公司是法國領先的無線ISP，並且在2002年至2012年期間擔任該職位。此外，他還是Polyconseil的創始人和首席技術官，該公司是一家電信咨詢公司，成立於2001年。

在此之前，他在多個領域擔任工程師職位：金融（JP Morgan和Société Générale）、國防（加拿大國防部）和醫療保健（血液輸注）。他出版了幾本技術書籍（關於C++、WiFi和互聯網架構），並在法國的一所工程學院擔任計算機科學講師。

一些有趣的事實：他教他的三個孩子用手指數二進制（最高可達1023），他在從事軟件工程之前學習了微生物學和進化遺傳學，而且他的降落傘在第二次跳傘時沒有打開。

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