Hands-On Machine Learning with Scikit-Learn, Keras, and Tensorflow: Concepts, Tools, and Techniques to Build Intelligent Systems, 3/e (Paperback)
暫譯: 實戰機器學習：使用 Scikit-Learn、Keras 和 TensorFlow 構建智能系統的概念、工具與技術，第 3 版 (平裝本)

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)

透過最近的一系列突破，深度學習提升了整個機器學習領域。現在，即使是對這項技術幾乎一無所知的程式設計師，也能使用簡單且高效的工具來實現能夠從數據中學習的程式。本書是暢銷書，使用具體的範例、最少的理論，以及可投入生產的 Python 框架——scikit-learn、Keras 和 TensorFlow，幫助您直觀地理解構建智能系統的概念和工具。

在這個更新的第三版中，作者 Aurelien Geron 探索了一系列技術，從簡單的線性回歸開始，逐步深入到深度神經網絡。書中有大量的程式碼範例和練習，幫助您應用所學的知識。您只需具備程式設計經驗即可開始。

- 使用 scikit-learn 追蹤一個範例機器學習專案的全過程
- 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
- 深入了解神經網絡架構，包括卷積網絡、遞歸網絡、生成對抗網絡和變壓器
- 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，並利用 Google 的 Vertex AI 進行大規模部署

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。作為前 Google 員工，他在 2013 年至 2016 年期間領導 YouTube 的影片分類團隊。他還曾於 2002 年至 2012 年擔任法國領先的無線 ISP Wifirst 的創始人及首席技術官，並於 2001 年創立了電信諮詢公司 Polyconseil，擔任首席技術官。

在此之前，他曾在多個領域擔任工程師：金融（JP Morgan 和 Société Générale）、國防（加拿大國防部）以及醫療保健（輸血）。他出版了幾本技術書籍（關於 C++、WiFi 和網際網路架構），並曾在法國的一所工程學校擔任計算機科學講師。

幾個有趣的事實：他教他的三個孩子用手指計算二進制（最多到 1023），他在進入軟體工程之前學習了微生物學和進化遺傳學，並且他的降落傘在第二次跳傘時沒有打開。

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