menu 简单麦麦
account_circle

使用Scikit Learn、Keras和TensorFlow进行机器学习-Hands-on Machine Learning with Scikit-Learn, Keras, and Tensor

帮助2581人找到了他们想要的文件
上传于 2020-03-02 3次下载 437次围观
文件编号:5926
文件详情
标题(title):Hands-on Machine Learning with Scikit-Learn, Keras, and TensorFlow
使用Scikit Learn、Keras和TensorFlow进行机器学习
作者(author):Aurelien Geron
出版社(publisher):O’reilly
大小(size):66 MB (69650561 bytes)
格式(extension):pdf
注意:如果文件下载完成后为无法打开的格式,请修改后缀名为格式对应后缀


Table of contents :
Machine Learning in Your Projects......Page 5
Objective and Approach......Page 6
Prerequisites......Page 7
Roadmap......Page 8
Changes in the Second Edition......Page 9
Other Resources......Page 10
Conventions Used in This Book......Page 12
Code Examples......Page 13
O’Reilly Online Learning......Page 14
How to Contact Us......Page 15
Acknowledgments......Page 16
I. The Fundamentals of Machine Learning......Page 20
1. The Machine Learning Landscape......Page 21
What Is Machine Learning?......Page 22
Why Use Machine Learning?......Page 23
Examples of Applications......Page 27
Types of Machine Learning Systems......Page 29
Supervised/Unsupervised Learning......Page 30
Batch and Online Learning......Page 39
Instance-Based Versus Model-Based Learning......Page 42
Insufficient Quantity of Training Data......Page 50
Nonrepresentative Training Data......Page 52
Poor-Quality Data......Page 54
Irrelevant Features......Page 55
Overfitting the Training Data......Page 56
Underfitting the Training Data......Page 58
Stepping Back......Page 59
Hyperparameter Tuning and Model Selection......Page 60
Data Mismatch......Page 62
Exercises......Page 63
Working with Real Data......Page 66
Look at the Big Picture......Page 68
Frame the Problem......Page 69
Select a Performance Measure......Page 71
Check the Assumptions......Page 74
Create the Workspace......Page 75
Download the Data......Page 79
Take a Quick Look at the Data Structure......Page 81
Create a Test Set......Page 85
Discover and Visualize the Data to Gain Insights......Page 91
Visualizing Geographical Data......Page 92
Looking for Correlations......Page 94
Experimenting with Attribute Combinations......Page 98
Data Cleaning......Page 100
Handling Text and Categorical Attributes......Page 104
Custom Transformers......Page 107
Feature Scaling......Page 108
Transformation Pipelines......Page 109
Training and Evaluating on the Training Set......Page 112
Better Evaluation Using Cross-Validation......Page 114
Fine-Tune Your Model......Page 116
Grid Search......Page 117
Randomized Search......Page 119
Analyze the Best Models and Their Errors......Page 120
Evaluate Your System on the Test Set......Page 121
Launch, Monitor, and Maintain Your System......Page 123
Try It Out!......Page 126
Exercises......Page 127
MNIST......Page 130
Training a Binary Classifier......Page 134
Measuring Accuracy Using Cross-Validation......Page 135
Confusion Matrix......Page 137
Precision and Recall......Page 140
Precision/Recall Trade-off......Page 141
The ROC Curve......Page 146
Multiclass Classification......Page 150
Error Analysis......Page 154
Multilabel Classification......Page 159
Multioutput Classification......Page 160
Exercises......Page 163
4. Training Models......Page 166
Linear Regression......Page 167
The Normal Equation......Page 169
Gradient Descent......Page 173
Batch Gradient Descent......Page 178
Stochastic Gradient Descent......Page 182
Mini-batch Gradient Descent......Page 186
Polynomial Regression......Page 188
Learning Curves......Page 191
Regularized Linear Models......Page 196
Ridge Regression......Page 197
Lasso Regression......Page 199
Elastic Net......Page 202
Early Stopping......Page 203
Estimating Probabilities......Page 205
Training and Cost Function......Page 207
Decision Boundaries......Page 208
Softmax Regression......Page 211
Exercises......Page 216
Linear SVM Classification......Page 219
Soft Margin Classification......Page 220
Nonlinear SVM Classification......Page 222
Polynomial Kernel......Page 224
Similarity Features......Page 225
Gaussian RBF Kernel......Page 226
Computational Complexity......Page 227
SVM Regression......Page 228
Decision Function and Predictions......Page 230
Training Objective......Page 231
Quadratic Programming......Page 233
Kernelized SVMs......Page 234
Online SVMs......Page 237
Exercises......Page 238
Training and Visualizing a Decision Tree......Page 240
Making Predictions......Page 241
Estimating Class Probabilities......Page 243
Computational Complexity......Page 244
Regularization Hyperparameters......Page 245
Regression......Page 247
Instability......Page 249
Exercises......Page 250
Voting Classifiers......Page 253
Bagging and Pasting......Page 257
Bagging and Pasting in Scikit-Learn......Page 258
Out-of-Bag Evaluation......Page 259
Random Patches and Random Subspaces......Page 260
Extra-Trees......Page 261
Feature Importance......Page 262
AdaBoost......Page 263
Gradient Boosting......Page 267
Stacking......Page 272
Exercises......Page 276
8. Dimensionality Reduction......Page 279
The Curse of Dimensionality......Page 280
Main Approaches for Dimensionality Reduction......Page 281
Projection......Page 282
Manifold Learning......Page 285
PCA......Page 286
Principal Components......Page 287
Projecting Down to d Dimensions......Page 289
Explained Variance Ratio......Page 290
Choosing the Right Number of Dimensions......Page 291
PCA for Compression......Page 292
Incremental PCA......Page 294
Kernel PCA......Page 295
Selecting a Kernel and Tuning Hyperparameters......Page 296
LLE......Page 299
Other Dimensionality Reduction Techniques......Page 301
Exercises......Page 303
9. Unsupervised Learning Techniques......Page 306
Clustering......Page 307
K-Means......Page 310
Limits of K-Means......Page 323
Using Clustering for Image Segmentation......Page 324
Using Clustering for Preprocessing......Page 326
Using Clustering for Semi-Supervised Learning......Page 328
DBSCAN......Page 332
Other Clustering Algorithms......Page 336
Gaussian Mixtures......Page 338
Anomaly Detection Using Gaussian Mixtures......Page 346
Selecting the Number of Clusters......Page 348
Bayesian Gaussian Mixture Models......Page 353
Other Algorithms for Anomaly and Novelty Detection......Page 358
Exercises......Page 360
II. Neural Networks and Deep Learning......Page 363
10. Introduction to Artificial Neural Networks with Keras......Page 364
From Biological to Artificial Neurons......Page 365
Biological Neurons......Page 366
Logical Computations with Neurons......Page 368
The Perceptron......Page 370
The Multilayer Perceptron and Backpropagation......Page 376
Regression MLPs......Page 380
Classification MLPs......Page 382
Implementing MLPs with Keras......Page 384
Installing TensorFlow 2......Page 386
Building an Image Classifier Using the Sequential API......Page 387
Building a Regression MLP Using the Sequential API......Page 401
Building Complex Models Using the Functional API......Page 402
Using the Subclassing API to Build Dynamic Models......Page 408
Saving and Restoring a Model......Page 410
Using Callbacks......Page 411
Using TensorBoard for Visualization......Page 414
Fine-Tuning Neural Network Hyperparameters......Page 418
Number of Hidden Layers......Page 423
Number of Neurons per Hidden Layer......Page 424
Learning Rate, Batch Size, and Other Hyperparameters......Page 425
Exercises......Page 428
11. Training Deep Neural Networks......Page 434
The Vanishing/Exploding Gradients Problems......Page 435
Glorot and He Initialization......Page 436
Nonsaturating Activation Functions......Page 438
Batch Normalization......Page 443
Gradient Clipping......Page 451
Reusing Pretrained Layers......Page 452
Transfer Learning with Keras......Page 455
Unsupervised Pretraining......Page 457
Pretraining on an Auxiliary Task......Page 459
Momentum Optimization......Page 460
Nesterov Accelerated Gradient......Page 462
AdaGrad......Page 464
Adam and Nadam Optimization......Page 466
Learning Rate Scheduling......Page 471
Avoiding Overfitting Through Regularization......Page 477
ℓ1 and ℓ2 Regularization......Page 478
Dropout......Page 479
Monte Carlo (MC) Dropout......Page 483
Max-Norm Regularization......Page 486
Summary and Practical Guidelines......Page 487
Exercises......Page 489
A Quick Tour of TensorFlow......Page 493
Using TensorFlow like NumPy......Page 497
Tensors and Operations......Page 498
Tensors and NumPy......Page 500
Type Conversions......Page 501
Variables......Page 502
Other Data Structures......Page 503
Custom Loss Functions......Page 504
Saving and Loading Models That Contain Custom Components......Page 506
Custom Activation Functions, Initializers, Regularizers, and Constraints......Page 508
Custom Metrics......Page 510
Custom Layers......Page 514
Custom Models......Page 518
Losses and Metrics Based on Model Internals......Page 521
Computing Gradients Using Autodiff......Page 524
Custom Training Loops......Page 529
TensorFlow Functions and Graphs......Page 533
AutoGraph and Tracing......Page 535
TF Function Rules......Page 537
Exercises......Page 539
13. Loading and Preprocessing Data with TensorFlow......Page 543
The Data API......Page 544
Chaining Transformations......Page 545
Shuffling the Data......Page 547
Preprocessing the Data......Page 551
Putting Everything Together......Page 553
Prefetching......Page 554
Using the Dataset with tf.keras......Page 556
The TFRecord Format......Page 558
A Brief Introduction to Protocol Buffers......Page 559
TensorFlow Protobufs......Page 561
Loading and Parsing Examples......Page 563
Handling Lists of Lists Using the SequenceExample Protobuf......Page 565
Preprocessing the Input Features......Page 566
Encoding Categorical Features Using One-Hot Vectors......Page 567
Encoding Categorical Features Using Embeddings......Page 570
Keras Preprocessing Layers......Page 576
TF Transform......Page 579
The TensorFlow Datasets (TFDS) Project......Page 581
Exercises......Page 582
14. Deep Computer Vision Using Convolutional Neural Networks......Page 587
The Architecture of the Visual Cortex......Page 588
Convolutional Layers......Page 589
Filters......Page 592
Stacking Multiple Feature Maps......Page 593
TensorFlow Implementation......Page 596
Memory Requirements......
下载方式
购买后可查看 购买按钮在底部

常见问题

  • question_answer
    解压密码,提取码在哪?
    keyboard_arrow_down
    • 均在下载旁边哦,请注意查看,如果没有则是不需要密码
  • question_answer
    文件不符合描述怎么办?
    keyboard_arrow_down
    • 如果有文件问题,可以通过 卖家联系方式 联系卖家,如果 联系不上卖家 或 卖家无法解决则可以在我的订单页面申请售后
  • question_answer
    其他
    keyboard_arrow_down
    • 3.本文件为公益分享,文件由网上采集而来,如有侵权等问题,请及时联系客服删除
      常见问题及官方客服联系方式:点击前往
      售后问题处理方式:点击前往
-到底部了哦-
微信扫码支付
使用Scikit Learn、Keras和TensorFlow进行机器学习-Hands-on Machine Learning with Scikit-Learn, Keras, and Tensor
支付金额: 共计:¥0.0

添加收藏

创建新合集