Scikit-Learn、Keras和TensorFlow的机器学习实用指南（第3版，影印版）
出版时间：2023年03月
页数：834
“研究机器学习的绝佳资源。你会从中发现清晰直观的解释以及大量的实用技巧。”
-Francois Chollet
深度学习库Keras的作者，Deep Learning with Python
一书的作者
“这本书出色地介绍了神经网络的理论和实践；推荐给任何对实用机器学习有兴趣的读者。”
-Pete Warden
TensorFlow的移动端负责人
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TensorFlow）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TensorFlow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
-Francois Chollet
深度学习库Keras的作者，Deep Learning with Python
一书的作者
“这本书出色地介绍了神经网络的理论和实践；推荐给任何对实用机器学习有兴趣的读者。”
-Pete Warden
TensorFlow的移动端负责人
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TensorFlow）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TensorFlow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
- 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
- Main Challenges of Machine Learning
- Testing and Validating
- Exercises
- 2. End-to-End Machine Learning Project
- Working with Real Data
- Look at the Big Picture
- Get the Data
- Explore and Visualize the Data to Gain Insights
- Prepare the Data for Machine Learning Algorithms
- Select and Train a Model
- Fine-Tune Your Model
- Launch, Monitor, and Maintain Your System
- Try It Out!
- Exercises
- 3. Classification
- MNIST
- Training a Binary Classifier
- Performance Measures
- Multiclass Classification
- Error Analysis
- Multilabel Classification
- Multioutput Classification
- Exercises
- 4. Training Model
- Linear Regression
- Gradient Descent
- Polynomial Regression
- Learning Curves
- Regularized Linear Models
- Logistic Regression
- Exercises
- 5. Support Vector Machines
- Linear SVM Classification
- Nonlinear SVM Classification
- SVM Regression
- Under the Hood of Linear SVM Classifiers
- The Dual Problem
- Exercises
- 6. Decision Trees
- Training and Visualizing a Decision Tree
- Making Predictions
- Estimating Class Probabilities
- The CART Training Algorithm
- Computational Complexity
- Gini Impurity or Entropy?
- Regularization Hyperparameters
- Regression
- Sensitivity to Axis Orientation
- Decision Trees Have a High Variance
- Exercises
- 7. Ensemble Learning and Random Forests
- Voting Classifiers
- Bagging and Pasting
- Random Forests
- Boosting
- Stacking
- Exercises
- 8. Dimensionality Reduction
- The Curse of Dimensionality
- Main Approaches for Dimensionality Reduction
- PCA
- Random Projection
- LLE
- Other Dimensionality Reduction Techniques
- Exercises
- 9. Unsupervised Learning Techniques
- Clustering Algorithms: k-means and DBSCAN
- Gaussian Mixtures
- Exercises
- Part II. Neural Networks and Deep Learning
- 10. Introduction to Artificial Neural Networks with Keras
- From Biological to Artificial Neurons
- Implementing MLPs with Keras
- Fine-Tuning Neural Network Hyperparameters
- Exercises
- 11. Training Deep Neural Networks
- The Vanishing/Exploding Gradients Problems
- Reusing Pretrained Layers
- Faster Optimizers
- Learning Rate Scheduling
- Avoiding Overfitting Through Regularization
- Summary and Practical Guidelines
- Exercises
- 12. Custom Models and Training with TensorFlow
- A Quick Tour of TensorFlow
- Using TensorFlow like NumPy
- Customizing Models and Training Algorithms
- TensorFlow Functions and Graphs
- Exercises
- 13. Loading and Preprocessing Data with TensorFlow
- The tf.data API
- The TFRecord Format
- Keras Preprocessing Layers
- The TensorFlow Datasets Project
- Exercises
- 14. Deep Computer Vision Using Convolutional Neural Networks
- The Architecture of the Visual Cortex
- Convolutional Layers
- Pooling Layers
- Implementing Pooling Layers with Keras
- CNN Architectures
- Implementing a ResNet-34 CNN Using Keras
- Using Pretrained Models from Keras
- Pretrained Models for Transfer Learning
- Classification and Localization
- Object Detection
- Object Tracking
- Semantic Segmentation
- Exercises
- 15. Processing Sequences Using RNNs and CNNs
- Recurrent Neurons and Layers
- Training RNNs
- Forecasting a Time Series
- Handling Long Sequences
- Exercises
- 16. Natural Language Processing with RNNs and Attention
- Generating Shakespearean Text Using a Character RNN
- Sentiment Analysis
- An Encoder–Decoder Network for Neural Machine Translation
- Attention Mechanisms
- An Avalanche of Transformer Models
- Vision Transformers
- Hugging Face’s Transformers Library
- Exercises
- 17. Autoencoders, GANs, and Diffusion Models
- Efficient Data Representations
- Performing PCA with an Undercomplete Linear Autoencoder
- Stacked Autoencoders
- Convolutional Autoencoders
- Denoising Autoencoders
- Sparse Autoencoders
- Variational Autoencoders
- Generating Fashion MNIST Images
- Generative Adversarial Networks
- Diffusion Models
- Exercises
- 18. Reinforcement Learning
- Learning to Optimize Rewards
- Policy Search
- Introduction to OpenAI Gym
- Neural Network Policies
- Evaluating Actions: The Credit Assignment Problem
- Policy Gradients
- Markov Decision Processes
- Temporal Difference Learning
- Q-Learning
- Implementing Deep Q-Learning
- Deep Q-Learning Variants
- Overview of Some Popular RL Algorithms
- Exercises
- 19. Training and Deploying TensorFlow Models at Scale
- Serving a TensorFlow Model
- Deploying a Model to a Mobile or Embedded Device
- Running a Model in a Web Page
- Using GPUs to Speed Up Computations
- Training Models Across Multiple Devices
- Exercises
- Thank You!
- A. Machine Learning Project Checklist
- B. Autodiff
- C. Special Data Structures
- D. TensorFlow Graphs
- Index
书名：Scikit-Learn、Keras和TensorFlow的机器学习实用指南（第3版，影印版）
作者：Aurélien Géron 著
国内出版社：东南大学出版社
出版时间：2023年03月
页数：834
书号：978-7-5766-0594-5
原版书书名：Hands-On Machine Learning with Scikit-Learn, Keras, and Tens
原版书出版商：O'Reilly Media
The animal on the cover of Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow is the fire salamander (Salamandra salamandra), an amphibian found across most of Europe. Its black, glossy skin features large yellow spots on the head and back, signaling the presence of alkaloid toxins. This is a possible source of this amphibian’s common name: contact with these toxins (which they can also spray short distances) causes convulsions and hyperventilation. Either the painful poisons or the moistness of the salamander’s skin (or both) led to a misguided belief that these creatures not only could survive being placed in fire but could extinguish it as well.
Fire salamanders live in shaded forests, hiding in moist crevices and under logs near the pools or other freshwater bodies that facilitate their breeding. Though they spend most of their lives on land, they give birth to their young in water. They subsist mostly on a diet of insects, spiders, slugs, and worms. Fire salamanders can grow up to a foot in length, and in captivity may live as long as 50 years.
The fire salamander’s numbers have been reduced by destruction of their forest habitat and capture for the pet trade, but the greatest threat they face is the susceptibility of their moisture-permeable skin to pollutants and microbes. Since 2014, they have become extinct in parts of the Netherlands and Belgium due to an introduced fungus.
Fire salamanders live in shaded forests, hiding in moist crevices and under logs near the pools or other freshwater bodies that facilitate their breeding. Though they spend most of their lives on land, they give birth to their young in water. They subsist mostly on a diet of insects, spiders, slugs, and worms. Fire salamanders can grow up to a foot in length, and in captivity may live as long as 50 years.
The fire salamander’s numbers have been reduced by destruction of their forest habitat and capture for the pet trade, but the greatest threat they face is the susceptibility of their moisture-permeable skin to pollutants and microbes. Since 2014, they have become extinct in parts of the Netherlands and Belgium due to an introduced fungus.
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