Scikit-Learn、Keras和TensorFlow的机器学习实用指南第2版

通过一系列的技术突破，深度学习促进了整个机器学习领域的发展。如今，即使对这种技术一无所知的程序员也可以使用简单、有效的工具来实现用数据进行学习的程序。这本畅销书的升级版借助具体示例、简介的理论和可用于生产的Python框架来帮助你直观地理解构建智能系统的概念和工具。

你将学习一系列可以快速使用的技术。通过每一章的练习来帮助你应用所学的知识，你所需要的只是编程经验。所有代码都可以再GitHub上找到，代码已经更新到TensorFlow 2和新版的Scikit-Learn。

通过Scikit-Learn和pandas的端到端项目学习机器学习基础知识

使用TensorFlow 2构建和训练若干神经网络架构，解决分类和回归问题

探索对象检测、语义分割、注意力机制、语言模型，生成对抗网络（GAN）等

探索Keras API,TensorFlow 2的官方高级API

使用TensorFlow的数据API、分布式策略API、TF Transform和TF-Serving来部署用于生产的TensorFlow模型

在Google Cloud 人工智能平台或移动设备上进行部署

探索无监督学习技术，如降维、聚类和异常检测

通过强化学习创建自主学习代理，包括使用TF-Agents库

“这本书是学习机器学习知识的jue佳资源。通过它你可以找到通俗易懂、直观浅显的解释，以及丰富的实用技巧。”——Francois Chollet(Keras作者，《Python深度学习》作者)

“这是一本解决神经网络问题的理论和实践的精彩入门书，我推荐每一位有兴趣学习实用机器学习知识的人阅读。”——Pete Warden (TensorFlow移动端开放团队负责人)

Preface
Part I. The Fundamentals of Machine Learning
1. The Machine Learning Landscape
What Is Machine Learning?
Why Use Machine Learning?
Examples of Applications
Types of Machine Learning Systems
Supervised/Unsupervised Learning
Batch and 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
Create the Workspace
Download the Data
Take a Quick Look at the Data Structure
Create a Test Set
Discover and Visualize the Data to Gain Insights
Visualizing Geographical Data
Looking for Correlations
Experimenting with Attribute Combinations
Prepare the Data for Machine Learning Algorithms
Data Cleaning
Handling Text and Categorical Attributes
Custom Transformers
Feature Scaling
Transformation Pipelines
Select and Train a Model
Training and Evaluating on the Training Set
Better Evaluation Using Cross-Validation
Fine-Tune Your Model
Grid Search
Randomized Search
Ensemble Methods
Analyze the Best Models and Their Errors
Evaluate Your System on the Test Set
Launch， Monitor， and Maintain Your System
Try It Out！
Exercises
3. Classification
MNIST
Training a Binary Classifier
Performance Measures
Measuring Accuracy Using Cross-Validation
Confusion Matrix
Precision and Recall
Precision/Recall Trade-off
The ROC Curve
Multiclass Classification
……

Part II. Neural Networks and Deep Learning
A. Exercise Solutions
B. Machine Learning Project Checklist
C. SVM Dual Problem
D. Autodiff
E. Other Popular ANN Architectures
F. Special Data Structures
G. TensorFIow Graphs
Index