Scikit-Learn、Keras和TensorFlow的机器学习实用指南第
更新日期:2024-07-13 00:59:56
书店:文轩网旗舰店
出版时间:2020-05
浏览量:1062
价格:0.0¥

书籍下载

内容介绍

作  者:(法)奥雷利安·吉翁 著
定  价:186
出 版 社:东南大学出版社
出版日期:2020年05月01日
页  数:819
装  帧:平装
ISBN:9787564188306
目录
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
lticlass 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
内容简介
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程序。这本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和新版的Scikit—Learn。