Python Machine Learning: Machine Learning and Deep Learning with Python, scikit-learn, and TensorFlow 2, 3/e (Paperback)

Sebastian Raschka, Vahid Mirjalili

Python Machine Learning: Machine Learning and Deep Learning with Python, scikit-learn, and TensorFlow 2, 3/e (Paperback)

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商品描述

Key Features

  • Third edition of the bestselling, widely acclaimed Python machine learning book
  • Clear and intuitive explanations take you deep into the theory and practice of Python machine learning
  • Fully updated and expanded to cover TensorFlow 2, Generative Adversarial Network models, reinforcement learning, and best practices

Book Description

Python Machine Learning, Third Edition is a comprehensive guide to machine learning and deep learning with Python. It acts as both a step-by-step tutorial, and a reference you'll keep coming back to as you build your machine learning systems.

Packed with clear explanations, visualizations, and working examples, the book covers all the essential machine learning techniques in depth. While some books teach you only to follow instructions, with this machine learning book, Raschka and Mirjalili teach the principles behind machine learning, allowing you to build models and applications for yourself.

Updated for TensorFlow 2.0, this new third edition introduces readers to its new Keras API features, as well as the latest additions to scikit-learn. It's also expanded to cover cutting-edge reinforcement learning techniques based on deep learning, as well as an introduction to GANs. Finally, this book also explores a subfield of natural language processing (NLP) called sentiment analysis, helping you learn how to use machine learning algorithms to classify documents.

This book is your companion to machine learning with Python, whether you're a Python developer new to machine learning or want to deepen your knowledge of the latest developments.

What you will learn

  • Master the frameworks, models, and techniques that enable machines to 'learn' from data
  • Use scikit-learn for machine learning and TensorFlow for deep learning
  • Apply machine learning to image classification, sentiment analysis, intelligent web applications, and more
  • Build and train neural networks, GANs, and other models
  • Discover best practices for evaluating and tuning models
  • Predict continuous target outcomes using regression analysis
  • Dig deeper into textual and social media data using sentiment analysis

Who This Book Is For

If you know some Python and you want to use machine learning and deep learning, pick up this book. Whether you want to start from scratch or extend your machine learning knowledge, this is an essential resource. Written for developers and data scientists who want to create practical machine learning and deep learning code, this book is ideal for anyone who wants to teach computers how to learn from data.

商品描述(中文翻譯)

《Python機器學習 第三版》的主要特點包括：

- 當紅暢銷的Python機器學習書籍的第三版
- 清晰直觀的解釋深入介紹Python機器學習的理論和實踐
- 完全更新和擴展，包括TensorFlow 2、生成對抗網絡模型、強化學習和最佳實踐

《書籍描述》

《Python機器學習 第三版》是一本全面介紹Python機器學習和深度學習的指南。它既是一個逐步教程，也是一本作為構建機器學習系統時不斷參考的資料。

這本書充滿了清晰的解釋、可視化和實例，深入介紹了所有基本的機器學習技術。儘管有些書籍只教你按照指示操作，但Raschka和Mirjalili在這本機器學習書中教授的是機器學習背後的原理，讓你能夠自己建立模型和應用。

這本新的第三版已經更新到TensorFlow 2.0，介紹了其新的Keras API功能，以及scikit-learn的最新增加。它還擴展到基於深度學習的尖端強化學習技術，以及生成對抗網絡的介紹。最後，這本書還探討了自然語言處理（NLP）的一個子領域，稱為情感分析，幫助你學習如何使用機器學習算法對文檔進行分類。

這本書是你學習Python機器學習的伴侶，無論你是一個新手Python開發者還是想深入了解最新發展的人。

《你將學到什麼》

- 掌握使機器能夠從數據中“學習”的框架、模型和技術
- 使用scikit-learn進行機器學習，使用TensorFlow進行深度學習
- 將機器學習應用於圖像分類、情感分析、智能網絡應用等領域
- 構建和訓練神經網絡、生成對抗網絡和其他模型
- 發現評估和調整模型的最佳實踐
- 使用回歸分析預測連續目標結果
- 通過情感分析深入挖掘文本和社交媒體數據

《適合閱讀對象》

如果你懂一些Python並且想使用機器學習和深度學習，那麼這本書非常適合你。無論你是從頭開始還是擴展你的機器學習知識，這是一個必不可少的資源。這本書適合開發人員和數據科學家，他們想要創建實用的機器學習和深度學習代碼，以及任何想要教電腦如何從數據中學習的人。

作者簡介

Sebastian Raschka is an Assistant Professor of Statistics at the University of Wisconsin-Madison focusing on machine learning and deep learning research. Some of his recent research methods have been applied to solving problems in the field of biometrics for imparting privacy to face images. Other research focus areas include the development of methods related to model evaluation in machine learning, deep learning for ordinal targets, and applications of machine learning to computational biology.

Vahid Mirjalili obtained his Ph.D. in mechanical engineering working on novel methods for large-scale, computational simulations of molecular structures. Currently, he is focusing his research efforts on applications of machine learning in various computer vision projects at the Department of Computer Science and Engineering at Michigan State University. He recently joined 3M Company as a research scientist, where he uses his expertise and applies state-of-the-art machine learning and deep learning techniques to solve real-world problems in various applications to make life better.

作者簡介(中文翻譯)

Sebastian Raschka 是威斯康辛大學麥迪遜分校的統計學助理教授，專注於機器學習和深度學習研究。他最近的研究方法已應用於解決生物特徵識別領域中的問題，以保護面部圖像的隱私。其他研究重點包括機器學習模型評估方法的開發，深度學習應用於有序目標的研究，以及機器學習在計算生物學中的應用。

Vahid Mirjalili 在機械工程領域獲得博士學位，研究大規模計算模擬分子結構的新方法。目前，他將研究重點放在密西根州立大學計算機科學與工程系的各種計算機視覺項目中應用機器學習。他最近加入了3M公司擔任研究科學家，利用自己的專業知識和最先進的機器學習和深度學習技術解決各種應用中的現實問題，以改善生活。

目錄大綱

Table of Contents

  1. Giving Computers the Ability to Learn from Data
  2. Training Simple ML Algorithms for Classification
  3. ML Classifiers Using scikit-learn
  4. Building Good Training Datasets - Data Preprocessing
  5. Compressing Data via Dimensionality Reduction
  6. Best Practices for Model Evaluation and Hyperparameter Tuning
  7. Combining Different Models for Ensemble Learning
  8. Applying ML to Sentiment Analysis
  9. Embedding a ML Model into a Web Application
  10. Predicting Continuous Target Variables with Regression Analysis
  11. Working with Unlabeled Data - Clustering Analysis
  12. Implementing Multilayer Artificial Neural Networks
  13. Parallelizing Neural Network Training with TensorFlow
  14. TensorFlow Mechanics
  15. Classifying Images with Deep Convolutional Neural Networks
  16. Modeling Sequential Data Using Recurrent Neural Networks
  17. GANs for Synthesizing New Data
  18. RL for Decision Making in Complex Environments

目錄大綱(中文翻譯)

目錄

1. 讓電腦從數據中學習的能力
2. 訓練簡單的機器學習算法進行分類
3. 使用scikit-learn進行機器學習分類器
4. 構建良好的訓練數據集 - 數據預處理
5. 通過降維壓縮數據
6. 模型評估和超參數調整的最佳實踐
7. 結合不同模型進行集成學習
8. 將機器學習應用於情感分析
9. 將機器學習模型嵌入網絡應用程序
10. 使用回歸分析預測連續目標變量
11. 處理無標籤數據 - 聚類分析
12. 實現多層人工神經網絡
13. 使用TensorFlow並行化神經網絡訓練
14. TensorFlow機制
15. 使用深度卷積神經網絡進行圖像分類
16. 使用循環神經網絡對序列數據進行建模
17. 使用GAN合成新數據
18. 在複雜環境中使用強化學習進行決策

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