Python Machine Learning By Example : Unlock machine learning best practices with real-world use cases, 4/e (Paperback)

Liu, Yuxi (Hayden)

  • 出版商: Packt Publishing
  • 出版日期: 2024-07-31
  • 售價: $1,850
  • 貴賓價: 9.5$1,758
  • 語言: 英文
  • 頁數: 518
  • 裝訂: Quality Paper - also called trade paper
  • ISBN: 1835085628
  • ISBN-13: 9781835085622
  • 相關分類: Python程式語言Machine Learning
  • 海外代購書籍(需單獨結帳)

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商品描述

Author Yuxi (Hayden) Liu teaches machine learning from the fundamentals to building NLP transformers and multimodal models with best practice tips and real-world examples using PyTorch, TensorFlow, scikit-learn, and pandas

Key Features:

- Discover new and updated content on NLP transformers, PyTorch, and computer vision modeling

- Includes a dedicated chapter on best practices and additional best practice tips throughout the book to improve your ML solutions

- Implement ML models, such as neural networks and linear and logistic regression, from scratch

- Purchase of the print or Kindle book includes a free PDF copy

Book Description:

The fourth edition of Python Machine Learning by Example is a comprehensive guide for beginners and experienced ML practitioners who want to learn more advanced techniques like multimodal modeling. Written by experienced machine learning author and ex-Google ML engineer Yuxi (Hayden) Liu, this edition emphasizes best practices, providing invaluable insights for ML engineers, data scientists, and analysts.

Explore advanced techniques, including two new chapters on natural language processing transformers with BERT and GPT, and multimodal computer vision models with PyTorch and Hugging Face. You'll learn key modeling techniques using practical examples, such as predicting stock prices and creating an image search engine.

This hands-on machine learning book navigates through complex challenges, bridging the gap between theoretical understanding and practical application. Elevate your machine learning and deep learning expertise, tackle intricate problems, and unlock the potential of advanced techniques in machine learning with this authoritative guide.

What You Will Learn:

- Follow machine learning best practices across data preparation and model development

- Build and improve image classifiers using Convolutional Neural Networks (CNNs) and transfer learning

- Develop and fine-tune neural networks using TensorFlow and PyTorch

- Analyze sequence data and make predictions using RNNs, transformers, and CLIP

- Build classifiers using SVMs and boost performance with PCA

- Avoid overfitting using regularization, feature selection, and more

Who this book is for:

This expanded fourth edition is ideal for data scientists, ML engineers, analysts, and students with Python programming knowledge. The real-world examples, best practices, and code prepare anyone undertaking their first serious ML project.

Table of Contents

- Getting Started with Machine Learning and Python

- Building a Movie Recommendation Engine

- Predicting Online Ad Click-Through with Tree-Based Algorithms

- Predicting Online Ad Click-Through with Logistic Regression

- Predicting Stock Prices with Regression Algorithms

- Predicting Stock Prices with Artificial Neural Networks

- Mining the 20 Newsgroups Dataset with Text Analysis Techniques

- Discovering Underlying Topics in the Newsgroups Dataset with Clustering and Topic Modeling

- Recognizing Faces with Support Vector Machine

- Machine Learning Best Practices

- Categorizing Images of Clothing with Convolutional Neural Networks

- Making Predictions with Sequences Using Recurrent Neural Networks

- Advancing Language Understanding and Generation with Transformer Models

- Building An Image Search Engine Using Multimodal Models

- Making Decisions in Complex Environments with Reinforcement Learning

商品描述(中文翻譯)

作者 Yuxi (Hayden) Liu 從基礎開始教授機器學習,涵蓋構建 NLP transformers 和多模態模型的最佳實踐技巧及實際案例,使用 PyTorch、TensorFlow、scikit-learn 和 pandas。

主要特色:
- 探索有關 NLP transformers、PyTorch 和計算機視覺建模的新內容和更新內容
- 包含專門章節介紹最佳實踐,並在全書中提供額外的最佳實踐技巧,以改善您的機器學習解決方案
- 從零開始實現機器學習模型,如神經網絡和線性及邏輯回歸
- 購買印刷版或 Kindle 版書籍可獲得免費 PDF 副本

書籍描述:
《Python 機器學習實例》第四版是一本全面的指南,適合希望學習更高級技術(如多模態建模)的初學者和經驗豐富的機器學習從業者。這一版由經驗豐富的機器學習作者及前 Google 機器學習工程師 Yuxi (Hayden) Liu 撰寫,強調最佳實踐,為機器學習工程師、數據科學家和分析師提供了寶貴的見解。

探索高級技術,包括兩個有關自然語言處理 transformers 的新章節,使用 BERT 和 GPT,以及使用 PyTorch 和 Hugging Face 的多模態計算機視覺模型。您將通過實際案例學習關鍵建模技術,例如預測股價和創建圖像搜索引擎。

這本實用的機器學習書籍將引導您克服複雜挑戰,架起理論理解與實際應用之間的橋樑。提升您的機器學習和深度學習專業知識,解決複雜問題,並利用這本權威指南釋放機器學習中高級技術的潛力。

您將學到的內容:
- 在數據準備和模型開發中遵循機器學習最佳實踐
- 使用卷積神經網絡 (CNNs) 和遷移學習構建和改進圖像分類器
- 使用 TensorFlow 和 PyTorch 開發和微調神經網絡
- 分析序列數據並使用 RNNs、transformers 和 CLIP 進行預測
- 使用支持向量機 (SVMs) 構建分類器,並通過主成分分析 (PCA) 提升性能
- 使用正則化、特徵選擇等方法避免過擬合

本書適合對象:
這一擴展的第四版非常適合具備 Python 編程知識的數據科學家、機器學習工程師、分析師和學生。書中的實際案例、最佳實踐和代碼為任何首次進行嚴肅機器學習項目的人做好準備。

目錄:
- 機器學習與 Python 入門
- 構建電影推薦引擎
- 使用樹基算法預測在線廣告點擊率
- 使用邏輯回歸預測在線廣告點擊率
- 使用回歸算法預測股價
- 使用人工神經網絡預測股價
- 使用文本分析技術挖掘 20 Newsgroups 數據集
- 使用聚類和主題建模發現 Newsgroups 數據集中的潛在主題
- 使用支持向量機識別面孔
- 機器學習最佳實踐
- 使用卷積神經網絡對服裝圖像進行分類
- 使用遞歸神經網絡進行序列預測
- 使用 transformer 模型推進語言理解和生成
- 使用多模態模型構建圖像搜索引擎
- 在複雜環境中使用強化學習做出決策