Mastering Machine Learning Algorithms, 2/e (Paperback)
暫譯: 掌握機器學習演算法（第二版）

Giuseppe Bonaccorso

Mastering Machine Learning Algorithms, 2/e (Paperback)

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商品描述

Mastering Machine Learning Algorithms, Second Edition helps you harness the real power of machine learning algorithms in order to implement smarter ways of meeting today's overwhelming data needs. This newly updated and revised guide will help you master algorithms used widely in semi-supervised learning, reinforcement learning, supervised learning, and unsupervised learning domains.

 

You will use all the modern libraries from the Python ecosystem – including NumPy and Keras – to extract features from varied complexities of data. Ranging from Bayesian models to the Markov chain Monte Carlo algorithm to Hidden Markov models, this machine learning book teaches you how to extract features from your dataset, perform complex dimensionality reduction, and train supervised and semi-supervised models by making use of Python-based libraries such as scikit-learn. You will also discover practical applications for complex techniques such as maximum likelihood estimation, Hebbian learning, and ensemble learning, and how to use TensorFlow 2.x to train effective deep neural networks.

 

By the end of this book, you will be ready to implement and solve end-to-end machine learning problems and use case scenarios

  • Understand the characteristics of a machine learning algorithm
  • Implement algorithms from supervised, semi-supervised, unsupervised, and RL domains
  • Learn how regression works in time-series analysis and risk prediction
  • Create, model, and train complex probabilistic models
  • Cluster high-dimensional data and evaluate model accuracy
  • Discover how artificial neural networks work – train, optimize, and validate them
  • Work with autoencoders, Hebbian networks, and GANs
  • Updated to include new algorithms and techniques
  • Code updated to Python 3.8 & TensorFlow 2.x
  • New coverage of regression analysis, time series analysis, deep learning models, and cutting-edge applications

商品描述(中文翻譯)

《掌握機器學習演算法（第二版）》幫助您利用機器學習演算法的真正力量，以實現更智能的方式來滿足當今龐大的數據需求。這本新更新和修訂的指南將幫助您掌握在半監督學習、強化學習、監督學習和非監督學習領域廣泛使用的演算法。

您將使用來自 Python 生態系統的所有現代庫，包括 NumPy 和 Keras，從不同複雜度的數據中提取特徵。從貝葉斯模型到馬可夫鏈蒙特卡羅演算法，再到隱馬可夫模型，這本機器學習書籍教您如何從數據集中提取特徵，執行複雜的降維，並利用基於 Python 的庫（如 scikit-learn）訓練監督和半監督模型。您還將發現複雜技術的實際應用，例如最大似然估計、Hebbian 學習和集成學習，以及如何使用 TensorFlow 2.x 訓練有效的深度神經網絡。

在本書結束時，您將準備好實施和解決端到端的機器學習問題和用例場景。

- 了解機器學習演算法的特性
- 實施來自監督、半監督、非監督和強化學習領域的演算法
- 學習回歸在時間序列分析和風險預測中的運作方式
- 創建、建模和訓練複雜的概率模型
- 對高維數據進行聚類並評估模型準確性
- 發現人工神經網絡的運作方式 - 訓練、優化和驗證它們
- 使用自編碼器、Hebbian 網絡和生成對抗網絡（GANs）
- 更新以包含新演算法和技術
- 代碼更新至 Python 3.8 和 TensorFlow 2.x
- 新增回歸分析、時間序列分析、深度學習模型和前沿應用的內容

作者簡介

Giuseppe Bonaccorso is Head of Data Science in a large multinational company. He received his M.Sc.Eng. in Electronics in 2005 from University of Catania, Italy, and continued his studies at University of Rome Tor Vergata, and University of Essex, UK. His main interests include machine/deep learning, reinforcement learning, big data, and bio-inspired adaptive systems. He is author of several publications including Machine Learning Algorithms and Hands-On Unsupervised Learning with Python, published by Packt.

作者簡介(中文翻譯)

Giuseppe Bonaccorso 是一家大型跨國公司的數據科學部門負責人。他於2005年在意大利卡塔尼亞大學獲得電子工程碩士學位，並在羅馬托爾維爾加大學和英國埃塞克斯大學繼續深造。他的主要研究興趣包括機器學習/深度學習、強化學習、大數據以及生物啟發的自適應系統。他是多部出版物的作者，包括《Machine Learning Algorithms》和《Hands-On Unsupervised Learning with Python》，這些書籍由Packt出版。

目錄大綱

  1. Machine Learning Model Fundamentals
  2. Loss functions and Regularization
  3. Introduction to Semi-Supervised Learning
  4. Advanced Semi-Supervised Classifiation
  5. Graph-based Semi-Supervised Learning
  6. Clustering and Unsupervised Models
  7. Advanced Clustering and Unsupervised Models
  8. Clustering and Unsupervised Models for Marketing
  9. Generalized Linear Models and Regression
  10. Introduction to Time-Series Analysis
  11. Bayesian Networks and Hidden Markov Models
  12. The EM Algorithm
  13. Component Analysis and Dimensionality Reduction
  14. Hebbian Learning
  15. Fundamentals of Ensemble Learning
  16. Advanced Boosting Algorithms
  17. Modeling Neural Networks
  18. Optimizing Neural Networks
  19. Deep Convolutional Networks
  20. Recurrent Neural Networks
  21. Auto-Encoders
  22. Introduction to Generative Adversarial Networks
  23. Deep Belief Networks
  24. Introduction to Reinforcement Learning
  25. Advanced Policy Estimation Algorithms

目錄大綱(中文翻譯)


  1. Machine Learning Model Fundamentals

  2. Loss functions and Regularization

  3. Introduction to Semi-Supervised Learning

  4. Advanced Semi-Supervised Classifiation

  5. Graph-based Semi-Supervised Learning

  6. Clustering and Unsupervised Models

  7. Advanced Clustering and Unsupervised Models

  8. Clustering and Unsupervised Models for Marketing

  9. Generalized Linear Models and Regression

  10. Introduction to Time-Series Analysis

  11. Bayesian Networks and Hidden Markov Models

  12. The EM Algorithm

  13. Component Analysis and Dimensionality Reduction

  14. Hebbian Learning

  15. Fundamentals of Ensemble Learning

  16. Advanced Boosting Algorithms

  17. Modeling Neural Networks

  18. Optimizing Neural Networks

  19. Deep Convolutional Networks

  20. Recurrent Neural Networks

  21. Auto-Encoders

  22. Introduction to Generative Adversarial Networks

  23. Deep Belief Networks

  24. Introduction to Reinforcement Learning

  25. Advanced Policy Estimation Algorithms

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