Scikit-Learn、Keras和TensorFlow的机器学习实用指南（第3版，影印版）
Scikit-Learn、Keras和TensorFlow的机器学习实用指南（第3版，影印版）
Aurélien Géron 著
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......展开全部内容介绍
  1. Preface
  2. Part I. The Fundamentals of Machine Learning
  3. 1. The Machine Learning Landscape
  4. What Is Machine Learning?
  5. Why Use Machine Learning?
  6. Examples of Applications
  7. Types of Machine Learning Systems
  8. Main Challenges of Machine Learning
  9. Testing and Validating
  10. Exercises
  11. 2. End-to-End Machine Learning Project
  12. Working with Real Data
  13. Look at the Big Picture
  14. Get the Data
  15. Explore and Visualize the Data to Gain Insights
  16. Prepare the Data for Machine Learning Algorithms
  17. Select and Train a Model
  18. Fine-Tune Your Model
  19. Launch, Monitor, and Maintain Your System
  20. Try It Out!
  21. Exercises
  22. 3. Classification
  23. MNIST
  24. Training a Binary Classifier
  25. Performance Measures
  26. Multiclass Classification
  27. Error Analysis
  28. Multilabel Classification
  29. Multioutput Classification
  30. Exercises
  31. 4. Training Model
  32. Linear Regression
  33. Gradient Descent
  34. Polynomial Regression
  35. Learning Curves
  36. Regularized Linear Models
  37. Logistic Regression
  38. Exercises
  39. 5. Support Vector Machines
  40. Linear SVM Classification
  41. Nonlinear SVM Classification
  42. SVM Regression
  43. Under the Hood of Linear SVM Classifiers
  44. The Dual Problem
  45. Exercises
  46. 6. Decision Trees
  47. Training and Visualizing a Decision Tree
  48. Making Predictions
  49. Estimating Class Probabilities
  50. The CART Training Algorithm
  51. Computational Complexity
  52. Gini Impurity or Entropy?
  53. Regularization Hyperparameters
  54. Regression
  55. Sensitivity to Axis Orientation
  56. Decision Trees Have a High Variance
  57. Exercises
  58. 7. Ensemble Learning and Random Forests
  59. Voting Classifiers
  60. Bagging and Pasting
  61. Random Forests
  62. Boosting
  63. Stacking
  64. Exercises
  65. 8. Dimensionality Reduction
  66. The Curse of Dimensionality
  67. Main Approaches for Dimensionality Reduction
  68. PCA
  69. Random Projection
  70. LLE
  71. Other Dimensionality Reduction Techniques
  72. Exercises
  73. 9. Unsupervised Learning Techniques
  74. Clustering Algorithms: k-means and DBSCAN
  75. Gaussian Mixtures
  76. Exercises
  77. Part II. Neural Networks and Deep Learning
  78. 10. Introduction to Artificial Neural Networks with Keras
  79. From Biological to Artificial Neurons
  80. Implementing MLPs with Keras
  81. Fine-Tuning Neural Network Hyperparameters
  82. Exercises
  83. 11. Training Deep Neural Networks
  84. The Vanishing/Exploding Gradients Problems
  85. Reusing Pretrained Layers
  86. Faster Optimizers
  87. Learning Rate Scheduling
  88. Avoiding Overfitting Through Regularization
  89. Summary and Practical Guidelines
  90. Exercises
  91. 12. Custom Models and Training with TensorFlow
  92. A Quick Tour of TensorFlow
  93. Using TensorFlow like NumPy
  94. Customizing Models and Training Algorithms
  95. TensorFlow Functions and Graphs
  96. Exercises
  97. 13. Loading and Preprocessing Data with TensorFlow
  98. The tf.data API
  99. The TFRecord Format
  100. Keras Preprocessing Layers
  101. The TensorFlow Datasets Project
  102. Exercises
  103. 14. Deep Computer Vision Using Convolutional Neural Networks
  104. The Architecture of the Visual Cortex
  105. Convolutional Layers
  106. Pooling Layers
  107. Implementing Pooling Layers with Keras
  108. CNN Architectures
  109. Implementing a ResNet-34 CNN Using Keras
  110. Using Pretrained Models from Keras
  111. Pretrained Models for Transfer Learning
  112. Classification and Localization
  113. Object Detection
  114. Object Tracking
  115. Semantic Segmentation
  116. Exercises
  117. 15. Processing Sequences Using RNNs and CNNs
  118. Recurrent Neurons and Layers
  119. Training RNNs
  120. Forecasting a Time Series
  121. Handling Long Sequences
  122. Exercises
  123. 16. Natural Language Processing with RNNs and Attention
  124. Generating Shakespearean Text Using a Character RNN
  125. Sentiment Analysis
  126. An Encoder–Decoder Network for Neural Machine Translation
  127. Attention Mechanisms
  128. An Avalanche of Transformer Models
  129. Vision Transformers
  130. Hugging Face’s Transformers Library
  131. Exercises
  132. 17. Autoencoders, GANs, and Diffusion Models
  133. Efficient Data Representations
  134. Performing PCA with an Undercomplete Linear Autoencoder
  135. Stacked Autoencoders
  136. Convolutional Autoencoders
  137. Denoising Autoencoders
  138. Sparse Autoencoders
  139. Variational Autoencoders
  140. Generating Fashion MNIST Images
  141. Generative Adversarial Networks
  142. Diffusion Models
  143. Exercises
  144. 18. Reinforcement Learning
  145. Learning to Optimize Rewards
  146. Policy Search
  147. Introduction to OpenAI Gym
  148. Neural Network Policies
  149. Evaluating Actions: The Credit Assignment Problem
  150. Policy Gradients
  151. Markov Decision Processes
  152. Temporal Difference Learning
  153. Q-Learning
  154. Implementing Deep Q-Learning
  155. Deep Q-Learning Variants
  156. Overview of Some Popular RL Algorithms
  157. Exercises
  158. 19. Training and Deploying TensorFlow Models at Scale
  159. Serving a TensorFlow Model
  160. Deploying a Model to a Mobile or Embedded Device
  161. Running a Model in a Web Page
  162. Using GPUs to Speed Up Computations
  163. Training Models Across Multiple Devices
  164. Exercises
  165. Thank You!
  166. A. Machine Learning Project Checklist
  167. B. Autodiff
  168. C. Special Data Structures
  169. D. TensorFlow Graphs
  170. Index
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定价：199.00元
书号：978-7-5766-0594-5
出版社：东南大学出版社
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