《Scikit-LearnKeras和TensorFlow的机器学习实用指南》[55M]百度网盘|pdf下载|亲测有效

内容简介

通过一系列最新的技术突破，深度学习推动了整个机器学习领域的发展。现在，即使是对这项技术几乎一无所知的程序员也可以使用简单、高效的工具来实现具备数据学习能力的程序。这本畅销书采用具体示例、最小化理论和生产就绪的Python框架（Scikit-Learn、Keras和TerisorFlow）来帮助你直观地理解构建智能系统的概念和工具。
在更新的第3版中，作者Aurélien Géron探究了一系列技术，从简单的线性回归开始，逐步推进到深度神经网络。书中的大量代码示例和练习有助于你学以致用。你需要具备一定的编程经验。
使用Scikit-Learn从头到尾跟踪一个机器学习示例项目
探索包括支持向量机、决策树、随机森林和集成方法在内的多种模型
利用降维、聚类和异常检测等无监督学习技术
深入研究包括卷积网络、递归网络、生成对抗网络、自动编码器、扩散模型和Transformers在内的多种神经网络架构
使用TensorFIow和Keras为计算机视觉、自然语言处理、生成模型和深度强化学习构建和训练神经网络

作者简介

奥雷利安·吉翁是一名机器学习顾问。作为一名前Google职员，在2013至2016年间，他领导了YouTube视频分类团队。在2002至2012年间，他是法国主要的无线ISP Wifirst的创始人和CT0，在2001年他还是Polyconseil的创始人和CT0，这家公司现在管理着电动汽车共享服务Autolib。

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
lndex

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