《Scikit-Learn、Keras和TensorFlow的機器學習實用指南》是2020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
- 作者：Aurélien
- 出版時間：2020年
- 出版社：東南大學出版社
- ISBN：9787564188306
- 類別：機器學習
- 開本：16 開
- 裝幀：平裝-膠訂
內容簡介,圖書目錄,作者簡介,
內容簡介
通過Scikit-Learn和pandas的端到端項目學習機器學習基礎知識
使用TensorFlow 2構建和訓練若干神經網路架構，解決分類和回歸問題
探索對象檢測、語義分割、注意力機制、語言模型，生成對抗網路（GAN）等
探索Keras API,TensorFlow 2的官方高級API
使用TensorFlow的數據API、分散式策略API、TF Transform和TF-Serving來部署用於生產的TensorFlow模型
在Google Cloud 人工智慧平台或移動設備上進行部署
探索無監督學習技術，如降維、聚類和異常檢測
通過強化學習創建自主學習代理，包括使用TF-Agents庫
圖書目錄
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
作者簡介
Aurélien Géron，是一名機器學習諮詢顧問和培訓師。作為一名前Google職員，在2013至2016年間，他領導了YouTube視頻分類團隊。在2002至2012年間，他身為法國主要的無線ISP Wifirst的創始人和CTO，在2001年他還是Polyconseil的創始人和CTO，這家公司現在管理著電動汽車共享服務Autolib'。
