Gespeichert in:
| Beteiligte Personen: | , |
|---|---|
| Format: | Elektronisch E-Book |
| Sprache: | Englisch |
| Veröffentlicht: |
New York
Apress
[2019]
|
| Schlagwörter: | |
| Links: | https://learning.oreilly.com/library/view/-/9781484251775/?ar |
| Zusammenfassung: | 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 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 |
| Beschreibung: | Includes bibliographical references and index. - Online resource; title from PDF title page (SpringerLink, viewed October 15, 2019) |
| Umfang: | 1 Online-Ressource illustrations |
| ISBN: | 9781484251775 1484251776 |
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| 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 | ||
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| language | English |
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| spelling | Alla, Sridhar VerfasserIn aut Beginning anomaly detection using Python-based deep learning with Keras and PyTorch Sridhar Alla, Suman Kalyan Adari New York Apress [2019] ©2019 1 Online-Ressource illustrations Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Includes bibliographical references and index. - Online resource; title from PDF title page (SpringerLink, viewed October 15, 2019) 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 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 Anomaly detection (Computer security) Python (Computer program language) Détection d'anomalies (Sécurité informatique) Python (Langage de programmation) Adari, Suman Kalyan VerfasserIn aut |
| spellingShingle | Alla, Sridhar Adari, Suman Kalyan Beginning anomaly detection using Python-based deep learning with Keras and PyTorch Anomaly detection (Computer security) Python (Computer program language) Détection d'anomalies (Sécurité informatique) Python (Langage de programmation) |
| title | Beginning anomaly detection using Python-based deep learning with Keras and PyTorch |
| title_auth | Beginning anomaly detection using Python-based deep learning with Keras and PyTorch |
| title_exact_search | Beginning anomaly detection using Python-based deep learning with Keras and PyTorch |
| title_full | Beginning anomaly detection using Python-based deep learning with Keras and PyTorch Sridhar Alla, Suman Kalyan Adari |
| title_fullStr | Beginning anomaly detection using Python-based deep learning with Keras and PyTorch Sridhar Alla, Suman Kalyan Adari |
| title_full_unstemmed | Beginning anomaly detection using Python-based deep learning with Keras and PyTorch Sridhar Alla, Suman Kalyan Adari |
| title_short | Beginning anomaly detection using Python-based deep learning |
| title_sort | beginning anomaly detection using python based deep learning with keras and pytorch |
| title_sub | with Keras and PyTorch |
| topic | Anomaly detection (Computer security) Python (Computer program language) Détection d'anomalies (Sécurité informatique) Python (Langage de programmation) |
| topic_facet | Anomaly detection (Computer security) Python (Computer program language) Détection d'anomalies (Sécurité informatique) Python (Langage de programmation) |
| work_keys_str_mv | AT allasridhar beginninganomalydetectionusingpythonbaseddeeplearningwithkerasandpytorch AT adarisumankalyan beginninganomalydetectionusingpythonbaseddeeplearningwithkerasandpytorch |