Beginning Anomaly Detection Using Python-Based Deep Learning. 2 Ed

Table of Contents....5 About the Authors....24 About the Technical Reviewers....25 Acknowledgments....27 Introduction....27 Chapter 1: Introduction to Anomaly Detection....30 What Is an Anomaly?....30 Anomalous Swans....30 Anomalies as Data Points....34 Anomalies in a Time Series....37 Personal Spending Pattern....38 Taxi Cabs....41 Categories of Anomalies....44 Data Point–Based Anomalies....44 Context-Based Anomalies....45 Pattern-Based Anomalies....47 Anomaly Detection....47 Outlier Detection....49 Noise Removal....49 Novelty Detection....50 Event Detection....50 Change Point Detection....50 Anomaly Score Calculation....52 The Three Styles of Anomaly Detection....52 Where Is Anomaly Detection Used?....53 Data Breaches....53 Identity Theft....55 Manufacturing....57 Networking....59 Medicine....59 Video Surveillance....60 Environment....60 Summary....60 Chapter 2: Introduction to Data Science....62 Data Science....63 Dataset....63 Pandas, Scikit-Learn, and Matplotlib....67 Data I/O....68 Data Loading....69 Data Saving....72 DataFrame Creation....72 Data Manipulation....75 Select....75 Filtering....89 Sorting....106 Applying Functions....120 Grouping....130 Combining DataFrames....135 Creating, Renaming, and Dropping Columns....147 Data Analysis....156 Value Counts....156 Pandas .describe() Method....157 Pandas Correlation Matrix....158 Visualization....162 Line Chart....162 Chart Customization....164 Scatter Plot....168 Histogram....169 Bar Graph....169 Data Processing....170 Nulls....171 Categorical Encoding....177 Scaling and Normalizing....186 Feature Engineering and Selection....190 Summary....202 Chapter 3: Introduction to Machine Learning....203 Machine Learning....204 Introduction to Machine Learning....204 Data Splitting....211 Modeling and Evaluation....212 Classification Metrics....217 Regression Metrics....226 Overfitting and Bias-Variance Tradeoff....228 Hyperparameter Tuning....240 Validation....244 Summary....247 Chapter 4: Traditional Machine Learning Algorithms....247 Traditional Machine Learning Algorithms....248 Isolation Forest....248 Example of an Isolation Forest....250 Anomaly Detection with an Isolation Forest....253 Data Preparation....254 Training....261 Hyperparameter Tuning....265 Evaluation and Summary....275 One-Class Support Vector Machine....279 How Does OC-SVM Work?....280 Anomaly Detection with OC-SVM....291 Data Preparation....291 Training....295 Hyperparameter Tuning....300 Evaluation and Summary....303 Summary....307 Chapter 5: Introduction to Deep Learning....308 Introduction to Deep Learning....310 What Is Deep Learning?....310 The Neuron....314 Activation Functions....317 Neural Networks....337 Loss Functions....347 Regression....347 Classification....349 Gradient Descent and Backpropagation....353 Loss Curve....371 Regularization....375 Optimizers....376 Multilayer Perceptron Supervised Anomaly Detection....390 Simple Neural Network: Keras....397 Simple Neural Network: PyTorch....408 Summary....418 Chapter 6: Autoencoders....418 What Are Autoencoders?....419 Simple Autoencoders....422 Sparse Autoencoders....444 Deep Autoencoders....448 Convolutional Autoencoders....450 Denoising Autoencoders....459 Variational Autoencoders....470 Summary....490 Chapter 7: Generative Adversarial Networks....491 What Is a Generative Adversarial Network?....492 Generative Adversarial Network Architecture....496 Wasserstein GAN....499 WGAN-GP....502 Anomaly Detection with a GAN....504 Summary....520 Chapter 8: Long Short-Term Memory Models....520 Sequences and Time Series Analysis....522 What Is an RNN?....525 What Is an LSTM?....526 LSTM for Anomaly Detection....534 Examples of Time Series....563 art_daily_no_noise.csv....564 art_daily_nojump.csv....565 art_daily_jumpsdown.csv....567 art_daily_perfect_square_wave.csv....570 art_load_balancer_spikes.csv....572 ambient_temperature_system_failure.csv....574 ec2_cpu_utilization.csv....576 rds_cpu_utilization.csv....577 Summary....579 Chapter 9: Temporal Convolutional Networks....579 What Is a Temporal Convolutional Network?....580 Dilated Temporal Convolutional Network....587 Anomaly Detection with the Dilated TCN....593 Encoder-Decoder Temporal Convolutional Network....615 Anomaly Detection with the ED-TCN....619 Summary....640 Chapter 10: Transformers....641 What Is a Transformer?....641 Transformer Architecture....646 Transformer Encoder....647 Transformer Decoder....655 Transformer Inference....658 Anomaly Detection with the Transformer....658 Summary....689 Chapter 11: Practical Use Cases and Future Trends of Anomaly Detection....689 Anomaly Detection....690 Real-World Use Cases of Anomaly Detection....694 Telecom....694 Banking....697 Environmental....699 Health Care....702 Transportation....707 Social Media....708 Finance and Insurance....710 Cybersecurity....711 Video Surveillance....716 Manufacturing....718 Smart Home....721 Retail....722 Implementation of Deep Learning–Based Anomaly Detection....722 Future Trends....725 Summary....727 Index....729