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Scikit-Learn、Keras和TensorFlow的机器学习实用指南第2版(影印版)(全2册)

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作者(法)奥雷利安·吉翁

出版社东南大学出版社

ISBN9787564188306

出版时间2020-05

装帧平装

开本16开

定价186元

货号1202080830

上书时间2024-09-02

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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.Spe Data Structures

G.TensorFIow Graphs

Index

内容摘要
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的示例、简洁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。所有代码都可以在GitHub上找到，代码已经更新到TensorFlow 2和近期新版的Scikit—Learn。