LEADER 04270nam 22006375 450 001 9910349526103321 005 20230804152358.0 010 $a9781484251775 010 $a1484251776 024 7 $a10.1007/978-1-4842-5177-5 035 $a(CKB)4100000009522837 035 $a(DE-He213)978-1-4842-5177-5 035 $a(MiAaPQ)EBC5940469 035 $a(CaSebORM)9781484251775 035 $a(PPN)272267783 035 $a(OCoLC)1142817864 035 $a(OCoLC)on1142817864 035 $a(EXLCZ)994100000009522837 100 $a20191010d2019 u| 0 101 0 $aeng 135 $aurnn|008mamaa 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aBeginning Anomaly Detection Using Python-Based Deep Learning $eWith Keras and PyTorch /$fby Sridhar Alla, Suman Kalyan Adari 205 $a1st ed. 2019. 210 1$aBerkeley, CA :$cApress :$cImprint: Apress,$d2019. 215 $a1 online resource (XVI, 416 p. 530 illus.) 300 $aIncludes index. 311 08$a9781484251768 311 08$a1484251768 320 $aIncludes bibliographical references. 327 $aChapter 1: What is Anomaly Detection? -- Chapter 2: Traditional Methods of Anomaly Detection -- Chapter 3: Introduction to Deep Learning -- Chapter 4: Autoencoders -- Chapter 5: Boltzmann Machines -- Chapter 6: Long Short-Term Memory Models -- Chapter 7: Temporal Convolutional Network -- Chapter 8: Practical Use Cases of Anomaly Detection -- Appendix A: Introduction to Keras -- Appendix B: Introduction to PyTorch. 330 $aUtilize this easy-to-follow beginner's guide to understand how deep learning can be applied to the task of anomaly detection. Using Keras and PyTorch in Python, the book focuses on how various deep learning models can be applied to semi-supervised and unsupervised anomaly detection tasks. This book begins with an explanation of what anomaly detection is, what it is used for, and its importance. After covering statistical and traditional machine learning methods for anomaly detection using Scikit-Learn in Python, the book then provides an introduction to deep learning with details on how to build and train a deep learning model in both Keras and PyTorch before shifting the focus to applications of the following deep learning models to anomaly detection: various types of Autoencoders, Restricted Boltzmann Machines, RNNs & LSTMs, and Temporal Convolutional Networks. The book explores unsupervised and semi-supervised anomaly detection along with the basics of time series-based anomaly detection. By the end of the book you will have a thorough understanding of the basic task of anomaly detection as well as an assortment of methods to approach anomaly detection, ranging from traditional methods to deep learning. Additionally, you are introduced to Scikit-Learn and are able to create deep learning models in Keras and PyTorch. What You'll Learn: Understand what anomaly detection is and why it is important in today's world Become familiar with statistical and traditional machine learning approaches to anomaly detection using Scikit-Learn Know the basics of deep learning in Python using Keras and PyTorch Be aware of basic data science concepts for measuring a model's performance: understand what AUC is, what precision and recall mean, and more Apply deep learning to semi-supervised and unsupervised anomaly detection. 606 $aArtificial intelligence 606 $aPython (Computer program language) 606 $aOpen source software 606 $aArtificial Intelligence 606 $aPython 606 $aOpen Source 615 0$aArtificial intelligence. 615 0$aPython (Computer program language) 615 0$aOpen source software. 615 14$aArtificial Intelligence. 615 24$aPython. 615 24$aOpen Source. 676 $a006.3 700 $aAlla$b Sridhar$4aut$4http://id.loc.gov/vocabulary/relators/aut$0886225 702 $aAdari$b Suman Kalyan$4aut$4http://id.loc.gov/vocabulary/relators/aut 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910349526103321 996 $aBeginning Anomaly Detection Using Python-Based Deep Learning$92507109 997 $aUNINA