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Beginning anomaly detection using Python-based deep learning : with Keras and PyTorch /
Chapter 5: Boltzmann Machines; What Is a Boltzmann Machine?; Restricted Boltzmann Machine (RBM); Anomaly Detection with the RBM - Credit Card Data Set; Anomaly Detection with the RBM - KDDCUP Data Set; Summary; Chapter 6: Long Short-Term Memory Models; Sequences and Time Series Analysis; What Is a R...
| Clasificación: | Libro Electrónico |
|---|---|
| Autores principales: | , |
| Formato: | Electrónico eBook |
| Idioma: | Inglés |
| Publicado: |
New York :
Apress,
[2019]
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| Temas: | |
| Acceso en línea: | Texto completo (Requiere registro previo con correo institucional) |
MARC
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| 100 | 1 | |a Alla, Sridhar, |e author. | |
| 245 | 1 | 0 | |a Beginning anomaly detection using Python-based deep learning : |b with Keras and PyTorch / |c Sridhar Alla, Suman Kalyan Adari. |
| 264 | 1 | |a New York : |b Apress, |c [2019] | |
| 264 | 4 | |c ©2019 | |
| 300 | |a 1 online resource : |b illustrations | ||
| 336 | |a text |b txt |2 rdacontent | ||
| 337 | |a computer |b c |2 rdamedia | ||
| 338 | |a online resource |b cr |2 rdacarrier | ||
| 588 | 0 | |a Online resource; title from PDF title page (SpringerLink, viewed October 15, 2019). | |
| 505 | 0 | |a Intro; Table of Contents; About the Authors; About the Technical Reviewers; Acknowledgments; Introduction; Chapter 1: What Is Anomaly Detection?; What Is an Anomaly?; Anomalous Swans; Anomalies as Data Points; Anomalies in a Time Series; Taxi Cabs; Categories of Anomalies; Data Point-Based Anomalies; Context-Based Anomalies; Pattern-Based Anomalies; Anomaly Detection; Outlier Detection; Noise Removal; Novelty Detection; The Three Styles of Anomaly Detection; Where Is Anomaly Detection Used?; Data Breaches; Identity Theft; Manufacturing; Networking; Medicine; Video Surveillance; Summary | |
| 505 | 8 | |a Chapter 2: Traditional Methods of Anomaly DetectionData Science Review; Isolation Forest; Mutant Fish; Anomaly Detection with Isolation Forest; One-Class Support Vector Machine; Anomaly Detection with OC-SVM; Summary; Chapter 3: Introduction to Deep Learning; What Is Deep Learning?; Artificial Neural Networks; Intro to Keras: A Simple Classifier Model; Intro to PyTorch: A Simple Classifier Model; Summary; Chapter 4: Autoencoders; What Are Autoencoders?; Simple Autoencoders; Sparse Autoencoders; Deep Autoencoders; Convolutional Autoencoders; Denoising Autoencoders; Variational Autoencoders | |
| 505 | 8 | |a Chapter 7: Temporal Convolutional NetworksWhat Is a Temporal Convolutional Network?; Dilated Temporal Convolutional Network; Anomaly Detection with the Dilated TCN; Encoder-Decoder Temporal Convolutional Network; Anomaly Detection with the ED-TCN; Summary; Chapter 8: Practical Use Cases of Anomaly Detection; Anomaly Detection; Real-World Use Cases of Anomaly Detection; Telecom; Banking; Environmental; Healthcare; Transportation; Social Media; Finance and Insurance; Cybersecurity; Video Surveillance; Manufacturing; Smart Home; Retail; Implementation of Deep Learning-Based Anomaly Detection | |
| 520 | |a Chapter 5: Boltzmann Machines; What Is a Boltzmann Machine?; Restricted Boltzmann Machine (RBM); Anomaly Detection with the RBM - Credit Card Data Set; Anomaly Detection with the RBM - KDDCUP Data Set; Summary; Chapter 6: Long Short-Term Memory Models; Sequences and Time Series Analysis; What Is a RNN?; What Is an LSTM?; LSTM for Anomaly Detection; Examples of Time Series; art_daily_no_noise; art_daily_nojump; art_daily_jumpsdown; art_daily_perfect_square_wave; art_load_balancer_spikes; ambient_temperature_system_failure; ec2_cpu_utilization; rds_cpu_utilization; Summary | ||
| 520 | |a Appendix A: Intro to Keras; What Is Keras?; Using Keras; Model Creation; Model Compilation and Training; Model Evaluation and Prediction; Layers; Input Layer; Dense Layer; Activation; Dropout; Flatten; Spatial Dropout 1D; Spatial Dropout 2D; Conv1D; Conv2D; UpSampling 1D; UpSampling 2D; ZeroPadding1D; ZeroPadding2D; MaxPooling1D; MaxPooling2D; Loss Functions; Mean Squared Error; Categorical Cross Entropy; Sparse Categorical Cross Entropy; Metrics; Binary Accuracy; Categorical Accuracy; Optimizers; SGD; Adam; RMSprop; Activations; Softmax; ReLU; Sigmoid; Callbacks; ModelCheckpoint | ||
| 504 | |a Includes bibliographical references and index. | ||
| 590 | |a O'Reilly |b O'Reilly Online Learning: Academic/Public Library Edition | ||
| 650 | 0 | |a Anomaly detection (Computer security) | |
| 650 | 0 | |a Python (Computer program language) | |
| 650 | 6 | |a Détection d'anomalies (Sécurité informatique) | |
| 650 | 6 | |a Python (Langage de programmation) | |
| 650 | 7 | |a Anomaly detection (Computer security) |2 fast |0 (OCoLC)fst01739215 | |
| 650 | 7 | |a Python (Computer program language) |2 fast |0 (OCoLC)fst01084736 | |
| 700 | 1 | |a Adari, Suman Kalyan, |e author. | |
| 856 | 4 | 0 | |u https://learning.oreilly.com/library/view/~/9781484251775/?ar |z Texto completo (Requiere registro previo con correo institucional) |
| 938 | |a Askews and Holts Library Services |b ASKH |n AH36903523 | ||
| 938 | |a ProQuest Ebook Central |b EBLB |n EBL5940469 | ||
| 938 | |a EBSCOhost |b EBSC |n 2271426 | ||
| 994 | |a 92 |b IZTAP | ||