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...
| Main Authors: | , |
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| Format: | eBook |
| Language: | English |
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New York
Apress
2019
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| Online Access: | |
| Collection: | O'Reilly - Collection details see MPG.ReNa |
| Summary: | 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 |
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| Physical Description: | 1 online resource illustrations |
| ISBN: | 9781484251775 |