標(biāo)題: Titlebook: Beginning Anomaly Detection Using Python-Based Deep Learning; Implement Anomaly De Suman Kalyan Adari,Sridhar Alla Book 2024Latest edition [打印本頁] 作者: Jefferson 時間: 2025-3-21 18:15
書目名稱Beginning Anomaly Detection Using Python-Based Deep Learning影響因子(影響力)
書目名稱Beginning Anomaly Detection Using Python-Based Deep Learning影響因子(影響力)學(xué)科排名
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書目名稱Beginning Anomaly Detection Using Python-Based Deep Learning網(wǎng)絡(luò)公開度學(xué)科排名
書目名稱Beginning Anomaly Detection Using Python-Based Deep Learning被引頻次
書目名稱Beginning Anomaly Detection Using Python-Based Deep Learning被引頻次學(xué)科排名
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書目名稱Beginning Anomaly Detection Using Python-Based Deep Learning年度引用學(xué)科排名
書目名稱Beginning Anomaly Detection Using Python-Based Deep Learning讀者反饋
書目名稱Beginning Anomaly Detection Using Python-Based Deep Learning讀者反饋學(xué)科排名
作者: obligation 時間: 2025-3-21 22:11
Introduction to Deep Learning,. These concepts will apply to the rest of the book and beyond. In the process, you will also implement a simple neural network model in both TensorFlow/Keras and PyTorch to perform supervised anomaly detection and serve as a gateway into learning how to model in these frameworks.作者: CHURL 時間: 2025-3-22 00:23
,Long Short-Term Memory?Models,ow they can be used to detect anomalies, and how you can implement anomaly detection using LSTM. You will work through several datasets depicting time series of different types of data, such as CPU utilization, taxi demand, etc., to illustrate how to detect anomalies. This chapter introduces you to 作者: nascent 時間: 2025-3-22 08:18
Practical Use Cases and Future Trends of Anomaly Detection,es can be used to address practical use cases and address real-life problems in the business landscape. Every business and use case is different, and we cannot simply copy and paste code and build a successful model to detect anomalies in any dataset, so this chapter covers many use cases to give yo作者: 帶來墨水 時間: 2025-3-22 09:35
like LSTM and‘TCN..Covers generative modeling via GANs and s.This beginner-oriented book will help you understand and perform anomaly detection by learning cutting-edge machine learning and deep learning techniques. This updated second edition focuses on supervised, semi-supervised, and unsupervised作者: 兇猛 時間: 2025-3-22 16:03 作者: AMBI 時間: 2025-3-22 19:05 作者: 公式 時間: 2025-3-22 23:16 作者: 為敵 時間: 2025-3-23 05:02 作者: Eosinophils 時間: 2025-3-23 09:26
Book 2024Latest editionques. This updated second edition focuses on supervised, semi-supervised, and unsupervised approaches to anomaly detection. Over the course of the book, you will learn how to use Keras and PyTorch in practical applications. It also introduces new chapters on GANs and transformers to reflect the late作者: 嚴(yán)厲譴責(zé) 時間: 2025-3-23 10:38
Book 2024Latest editionhine learning and deep learning, and how to implement multilayer perceptrons for supervised anomaly detection in both Keras and PyTorch. From here, the focus shifts to the applications of deep learning models for anomaly detection, including various types of autoencoders,?recurrent neural networks (作者: Camouflage 時間: 2025-3-23 16:42 作者: 打折 時間: 2025-3-23 18:40 作者: Phenothiazines 時間: 2025-3-23 22:15 作者: octogenarian 時間: 2025-3-24 02:54 作者: 不連貫 時間: 2025-3-24 10:23 作者: Limited 時間: 2025-3-24 13:08
Introduction to Machine Learning,every modeling task you may come across, and they extend into deep learning modeling as well. This is a high-level theoretical introduction to machine learning, since the practical material and implementation of these machine learning principles will be covered in the subsequent chapters.作者: eczema 時間: 2025-3-24 18:55
Introduction to Deep Learning,. These concepts will apply to the rest of the book and beyond. In the process, you will also implement a simple neural network model in both TensorFlow/Keras and PyTorch to perform supervised anomaly detection and serve as a gateway into learning how to model in these frameworks.作者: clarify 時間: 2025-3-24 22:40
https://doi.org/10.1007/979-8-8688-0008-5Anamoly Detection; Deep Learning; Python; Keras; PyTorch; Novelty detection; Auto Encoders; Fraud Detection作者: 緯線 時間: 2025-3-25 00:30 作者: subordinate 時間: 2025-3-25 04:38
Qian Feng,Dongjing Cao,Shulong Bao,Lu LiuIn this chapter, you will learn about anomalies in general, the categories of anomalies, and anomaly detection. You will also learn why anomaly detection is important, how anomalies can be detected, and the use case for such a mechanism.作者: 祖先 時間: 2025-3-25 09:38
https://doi.org/10.1007/978-981-19-5096-4This chapter introduces you to the isolation forest and the one-class support vector machine algorithms and walks you through how to use them for anomaly detection. In the process, you will also practice incorporating the fundamental machine learning workflow and incorporating hyperparameter tuning using the validation set.作者: Generator 時間: 2025-3-25 15:09 作者: 效果 時間: 2025-3-25 15:49
Xinru Liu,Mingtao Pei,Wei Liang,Zhengang NieIn this chapter, you will learn about generative adversarial networks as well as how you can implement anomaly detection using them.作者: BLAND 時間: 2025-3-25 22:26 作者: 使絕緣 時間: 2025-3-26 01:59
Jun Lin,Zhengyong Feng,Jialiang TangIn this chapter, you will learn about transformer networks and how you can implement anomaly detection using a transformer.作者: 語源學(xué) 時間: 2025-3-26 04:55
Introduction to Anomaly Detection,In this chapter, you will learn about anomalies in general, the categories of anomalies, and anomaly detection. You will also learn why anomaly detection is important, how anomalies can be detected, and the use case for such a mechanism.作者: 細(xì)菌等 時間: 2025-3-26 11:06 作者: Gourmet 時間: 2025-3-26 12:49
Autoencoders,In this chapter, you will learn about autoencoder neural networks and the different types of autoencoders. You will also learn how autoencoders can be used to detect anomalies and how you can implement anomaly detection using autoencoders.作者: TEM 時間: 2025-3-26 17:50 作者: negligence 時間: 2025-3-26 22:24 作者: 健忘癥 時間: 2025-3-27 03:00
Transformers,In this chapter, you will learn about transformer networks and how you can implement anomaly detection using a transformer.作者: Free-Radical 時間: 2025-3-27 05:51 作者: Palpitation 時間: 2025-3-27 10:13 作者: pulse-pressure 時間: 2025-3-27 15:40
https://doi.org/10.1007/978-981-33-6033-4every modeling task you may come across, and they extend into deep learning modeling as well. This is a high-level theoretical introduction to machine learning, since the practical material and implementation of these machine learning principles will be covered in the subsequent chapters.作者: 歡樂東方 時間: 2025-3-27 21:22
Fei Song,Qiang Chen,Tao Lei,Zhenming Peng. These concepts will apply to the rest of the book and beyond. In the process, you will also implement a simple neural network model in both TensorFlow/Keras and PyTorch to perform supervised anomaly detection and serve as a gateway into learning how to model in these frameworks.作者: 扔掉掐死你 時間: 2025-3-28 01:55
Bowen Zhang,Shuyi Li,Zhuming Wang,Lifang Wuow they can be used to detect anomalies, and how you can implement anomaly detection using LSTM. You will work through several datasets depicting time series of different types of data, such as CPU utilization, taxi demand, etc., to illustrate how to detect anomalies. This chapter introduces you to 作者: 凝視 時間: 2025-3-28 05:40
https://doi.org/10.1007/978-981-99-7549-5es can be used to address practical use cases and address real-life problems in the business landscape. Every business and use case is different, and we cannot simply copy and paste code and build a successful model to detect anomalies in any dataset, so this chapter covers many use cases to give yo作者: 格言 時間: 2025-3-28 09:09
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