- Scikit-Learn Keras和TensorFlow的机器学习实用指南(影印版上下第3版)(英文版)
- - 作者：(法)奥雷利安·吉翁|责编:张烨
  - 出版社：东南大学
  - ISBN：9787576605945
  - 出版日期：2023/03/01
  - 页数：834
- 售价：79.6

内容大纲
    通过一系列最新的技术突破，深度学习推动了整个机器学习领域的发展。现在，即使是对这项技术几乎一无所知的程序员也可以使用简单、高效的工具来实现具备数据学习能力的程序。这本畅销书采用具体示例、最小化理论和生产就绪的Python框架（Scikit-Learn、Keras和TerisorFlow）来帮助你直观地理解构建智能系统的概念和工具。
    在更新的第3版中，作者Aurélien Géron探究了一系列技术，从简单的线性回归开始，逐步推进到深度神经网络。书中的大量代码示例和练习有助于你学以致用。你需要具备一定的编程经验。
    使用Scikit-Learn从头到尾跟踪一个机器学习示例项目
    探索包括支持向量机、决策树、随机森林和集成方法在内的多种模型
    利用降维、聚类和异常检测等无监督学习技术
    深入研究包括卷积网络、递归网络、生成对抗网络、自动编码器、扩散模型和Transformers在内的多种神经网络架构
    使用TensorFIow和Keras为计算机视觉、自然语言处理、生成模型和深度强化学习构建和训练神经网络
作者介绍
奥雷利安·吉翁是一名机器学习顾问。作为一名前Google职员，在2013至2016年间，他领导了YouTube视频分类团队。在2002至2012年间，他是法国主要的无线ISP Wifirst的创始人和CT0，在2001年他还是Polyconseil的创始人和CT0，这家公司现在管理着电动汽车共享服务Autolib。
目录
Preface
Part Ⅰ．The Fundamentals of Machine Learning
1．TheMachine Learning Landscape
  What Is Machine Learning
  Whr Use Machine Learning
  Examples of Applications
  Types of Machine Learning Systems
  Training Supervision
  Batch Versus Online Learning
  Instance Based Versus Model Based Learning
  Main Challenges of Machine Learning
  Insufficient Quantity of Training Data
  NonrepresentatiVe Training Data
  Poor-Quality Data
  Irrelevant Features
  Overfitting the Training Data
  Underfitting the Training Data
  Stepping Back
  Testing and Validating
  Hyperparameter Tuning and Model Selection
  Data Mismatch
  Exercises
2．End-to-End Machine Learning Project
  Working with Real Data
  Look at the Big Picture
  Frame the Problem
  Select a Performance Measure
  Check the Assumptions
  Get the Data
  Running the Code Examples Using Google Colab
  Saving Your Code Changes and Your Data
  The Power and Danger of Interactivity
  Book Code Versus Notebook Code
  Download the Data
  Take a Quick Look at the Data Structure
  Create a 11est Set
  Explore and Visualize the Data to Gain Insights
  Visualizing Geographical Data
  Look for Correlations
  Experiment with Attribute Combinations
  Prepare the Data for Machine Learning Algorithms
  Clean the Data
  Handling Text and Categorical Attributes
  Feature Scaling and Transformation
  Custom Transformers
  Transformation Pipelines
  Select and Train a Model
  Train and Evaluate on the Training Set
  Better Evaluation Using Cross-Validation
  Fine-Tune Your Model

  Grid Search
  Randomized Search
  Ensemble Methods
  Analyzing the Best Models and Their Errors
  Evaluate Your System on the Test Set
  Launch，Monitor,and Maintain Your System
  TryItout
  Exercises
3．Classification
  MNIST
  Training a Binary Classifier
  Performance Measures
  Measuring Accuracy Using Cross-Validation
  Confusion Matrices
  Precision and Recall
  The Precision／Recall Trade-off
  The ROC Curve
  Multiclass Classification
  Error Analysis
  Multilabel Classification
  Multioutput Classification
  Exercises
……
Part Ⅱ Neural Networks and Deep Learning
A．Machine Learning Project Checklist
B．Autodiff
C．SpecialData Structures
D．TensorFIowGraphs
lndex

内容大纲

作者介绍

目录

同类热销排行榜

推荐书目