《Scikit-Learn Keras和TensorFlow的機器學習實用指南（影印版上下第3版）》是2023年東南大學出版社出版的圖書。
基本介紹
- 中文名：Scikit-Learn Keras和TensorFlow的機器學習實用指南（影印版上下第3版）
- 出版時間：2023年3月1日
- 出版社：東南大學出版社
- 頁數：834 頁
- ISBN：9787576605945
- 定價：199 元
- 開本：16 開
- 裝幀：平裝
內容簡介,圖書目錄,
內容簡介
通過一系列最新的技術突破，深度學習推動了整個機器學習領域的發展。現在，即使是對這項技術幾乎一無所知的程式設計師也可以使用簡單、高效的工具來實現具備數據學習能力的程式。這本暢銷書採用具體示例、最小化理論和生產就緒的Python框架（Scikit-Learn、Keras和TerisorFlow）來幫助你直觀地理解構建智慧型系統的概念和工具。
在更新的第3版中，作者Aurélien Géron探究了一系列技術，從簡單的線性回歸開始，逐步推進到深度神經網路。書中的大量代碼示例和練習有助於你學以致用。你需要具備一定的編程經驗。
使用Scikit-Learn從頭到尾跟蹤一個機器學習示例項目
探索包括支持向量機、決策樹、隨機森林和集成方法在內的多種模型
利用降維、聚類和異常檢測等無監督學習技術
深入研究包括卷積網路、遞歸網路、生成對抗網路、自動編碼器、擴散模型和Transformers在內的多種神經網路架構
使用TensorFIow和Keras為計算機視覺、自然語言處理、生成模型和深度強化學習構建和訓練神經網路
圖書目錄
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
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