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Beginning Anomaly Detection Using Python-Based Deep Learning. With Keras and PyTorch
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Sridhar Alla; Suman Kalyan Adari
Год выпуска
2026
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Печатная книга «Beginning Anomaly Detection Using Python-Based Deep Learning. With Keras and PyTorch» в мягкой обложке, язык - английский. В книге 434 страниц, она напечатана на белой офсетной бумаге
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Beginning Anomaly Detection Using Python-Based Deep Learning. With Keras and PyTorchПечатная книга «Beginning Anomaly Detection Using Python-Based Deep Learning. With Keras and PyTorch» в мягкой обложке, язык - английский. В книге 434 страниц, она напечатана на белой офсетной бумаге плотностью 80 г/м². Формат издания — 205x260 мм. Все книги аккуратно упаковываем в прочный трёхслойный непрозрачный картонный конверт.
Chapter 1: What is Anomaly Detection?Chapter Goal: Introduce reader to the task of anomaly detection, where it's used, why it's important, as well as the different types of "anomaly detection" there are. No of pages: 30Sub -Topics1. What is an anomaly2. Use cases today3. Different types of anomaliesChapter 2: Traditional Methods of Anomaly DetectionChapter Goal: Introduce reader to a couple high performing traditional methods of anomaly detection in Scikit-Learn. Evaluation metrics are performed on both (will be set as the benchmark of comparison for the deep learning models later on)No of pages: 50Sub - Topics1. Isolation Forest2. One class support vector machine3. Mahalanobis distance based anomaly detection Chapter 3: Intro to Keras and PyTorchChapter Goal: Introduce reader to deep learning and how to build, train a basic model in both Keras and in PyTorch. Additionally, perform evaluation metrics on both. Also, discuss the various deep learning models that can be applied to semi-supervised and unsupervised anomaly detection_No of pages : 40Sub - Topics: 1_What is deep learning?2. Intro to Keras: simple classifier model3. Intro to PyTorch: simple classifier model4. How can we apply deep learning to anomaly detection? Chapter 4: AutoencodersChapter Goal: Introduce reader to several autoencoders and how they can perform anomaly detection in both semi-supervised and unsupervised anomaly detection_No of pages: 40Sub - Topics: 1. What are autoencoders?2. Basic autoencoder3. Denoising autoencoder4. Variational autoencoder5. Summary of autoencoders as a modelChapter 5: Boltzmann MachinesChapter Goal: Introduce reader to a restricted Boltzmann machine, deep Boltzmann machine, and a deep belief network_No of pages: 30Sub - Topics: 1. What is a Boltzmann machine?2. RBM3. DBM4. DBN5. Summary of the modelsChapter 6: Time-Series Anomaly DetectionChapter Goal: Introduce reader to RNNs and LSTMs for time series anomaly detection_No of pages:Sub - Topics: 301. What is a time series and how do we detect anomalies in that?2. What is an RNN3. RNN application4. What is an LSTM?5. LSTM application 6. Summary of the modelsChapter 7: Temporal Convolutional NetworkChapter Goal: Introduce reader to the TCN and how it can be used in anomaly detection_No of pages: 30Sub - Topics: 1. What is a TCN?2. Encoder-Decoder TCN3. Dilated TCN4. Summary of modelsChapter 8: Practical Use Cases of Anomaly DetectionChapter Goal: Illustrate common use cases_No of pages: 30Sub - Topics: 1. Use casesAppendix A: Introduction to KerasChapter Goal: Introduce reader to the Keras No of pages: 30Sub - Topics: 1. What is a Keras?2. How to use itAppendix B: Introduction to PyTorchChapter Goal: Introduce reader to the PyTorchNo of pages: 30Sub - Topics: 1. What is a PyTorch?2. How to use it
Chapter 1: What is Anomaly Detection?Chapter Goal: Introduce reader to the task of anomaly detection, where it's used, why it's important, as well as the different types of "anomaly detection" there are. No of pages: 30Sub -Topics1. What is an anomaly2. Use cases today3. Different types of anomaliesChapter 2: Traditional Methods of Anomaly DetectionChapter Goal: Introduce reader to a couple high performing traditional methods of anomaly detection in Scikit-Learn. Evaluation metrics are performed on both (will be set as the benchmark of comparison for the deep learning models later on)No of pages: 50Sub - Topics1. Isolation Forest2. One class support vector machine3. Mahalanobis distance based anomaly detection Chapter 3: Intro to Keras and PyTorchChapter Goal: Introduce reader to deep learning and how to build, train a basic model in both Keras and in PyTorch. Additionally, perform evaluation metrics on both. Also, discuss the various deep learning models that can be applied to semi-supervised and unsupervised anomaly detection_No of pages : 40Sub - Topics: 1_What is deep learning?2. Intro to Keras: simple classifier model3. Intro to PyTorch: simple classifier model4. How can we apply deep learning to anomaly detection? Chapter 4: AutoencodersChapter Goal: Introduce reader to several autoencoders and how they can perform anomaly detection in both semi-supervised and unsupervised anomaly detection_No of pages: 40Sub - Topics: 1. What are autoencoders?2. Basic autoencoder3. Denoising autoencoder4. Variational autoencoder5. Summary of autoencoders as a modelChapter 5: Boltzmann MachinesChapter Goal: Introduce reader to a restricted Boltzmann machine, deep Boltzmann machine, and a deep belief network_No of pages: 30Sub - Topics: 1. What is a Boltzmann machine?2. RBM3. DBM4. DBN5. Summary of the modelsChapter 6: Time-Series Anomaly DetectionChapter Goal: Introduce reader to RNNs and LSTMs for time series anomaly detection_No of pages:Sub - Topics: 301. What is a time series and how do we detect anomalies in that?2. What is an RNN3. RNN application4. What is an LSTM?5. LSTM application 6. Summary of the modelsChapter 7: Temporal Convolutional NetworkChapter Goal: Introduce reader to the TCN and how it can be used in anomaly detection_No of pages: 30Sub - Topics: 1. What is a TCN?2. Encoder-Decoder TCN3. Dilated TCN4. Summary of modelsChapter 8: Practical Use Cases of Anomaly DetectionChapter Goal: Illustrate common use cases_No of pages: 30Sub - Topics: 1. Use casesAppendix A: Introduction to KerasChapter Goal: Introduce reader to the Keras No of pages: 30Sub - Topics: 1. What is a Keras?2. How to use itAppendix B: Introduction to PyTorchChapter Goal: Introduce reader to the PyTorchNo of pages: 30Sub - Topics: 1. What is a PyTorch?2. How to use it
- Артикул
- 5193107299
- Автор на обложке
- Sridhar Alla; Suman Kalyan Adari
- Год выпуска
- 2026
- Тип бумаги в книге
- Офсетная
- Тип обложки
- Мягкая обложка
- Тип книги
- Печать по требованию
- Язык издания
- Английский
- Количество страниц
- 434
- Размеры, мм
- 205x260
- Вес товара, г
- 1048
- ISBN
- 9781484251768
- Возрастные ограничения
- 12+
Подборки товаров в категории Книги: Компьютерные технологии
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Beginning Anomaly Detection Using Python-Based Deep Learning. With Keras and PyTorch
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