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  • 1
    Online Resource
    Online Resource
    Beginning Anomaly Detection Using Python-Based Deep Learning : With Keras and PyTorch (2019)
    [Erscheinungsort nicht ermittelbar] : Apress | Boston, MA : Safari
    Details
    ISBN: 9781484251775
    Language: English
    Pages: 1 online resource (427 pages)
    Edition: 1st edition
    Keywords: Electronic books ; local ; Electronic books
    Abstract: Utilize 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 Will 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 Who This Book Is For Data scientists and machine learning engineers interested in learning the basics of deep learning applications in anomaly detection
    Note: Online resource; Title from title page (viewed October 10, 2019)
    URL: lizenzpflichtig
    Library Location Call Number Volume/Issue/Year Availability
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    MPI Ethno. Forsch.
  • 2
    Online Resource
    Online Resource
    Beginning anomaly detection using Python-based deep learning : implement anomaly detection applications with Keras and PyTorch (2024)
    New York, NY : Apress
    Details
    ISBN: 9798868800085
    Language: English
    Pages: 1 online resource (538 pages) , illustrations
    Edition: Second edition.
    Parallel Title: Erscheint auch als
    Keywords: Anomaly detection (Computer security) ; Python (Computer program language) ; Machine learning ; Détection d'anomalies (Sécurité informatique) ; Python (Langage de programmation) ; Apprentissage automatique
    Abstract: 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 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 latest trends in deep learning. Beginning Anomaly Detection Using Python-Based Deep Learning begins with an introduction to anomaly detection, its importance, and its applications. It then covers core data science and machine learning modeling concepts before delving into traditional machine learning algorithms such as OC-SVM and Isolation Forest for anomaly detection using scikit-learn. Following this, the authors explain the essentials of machine 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 (via LSTM), temporal convolutional networks, and transformers, with the latter three architectures applied to time-series anomaly detection. This edition has a new chapter on GANs (Generative Adversarial Networks), as well as new material covering transformer architecture in the context of time-series anomaly detection. After completing this book, you will have a thorough understanding of anomaly detection as well as an assortment of methods to approach it in various contexts, including time-series data. Additionally, you will have gained an introduction to scikit-learn, GANs, transformers, Keras, and PyTorch, empowering you to create your own machine learning- or deep learning-based anomaly detectors. What You Will Learn Understand what anomaly detection is, why it it is important, and how it is applied Grasp the core concepts of machine learning. Master traditional machine learning approaches to anomaly detection using scikit-kearn. Understand deep learning in Python using Keras and PyTorch Process data through pandas and evaluate your model's performance using metrics like F1-score, precision, and recall Apply deep learning to supervised, semi-supervised, and unsupervised anomaly detection tasks for tabular datasets and time series applications Who This Book Is For Data scientists and machine learning engineers of all levels of experience interested in learning the basics of deep learning applications in anomaly detection.
    Note: Includes index
    URL: lizenzpflichtig
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    MPI Ethno. Forsch.
  • 3
    Online Resource
    Online Resource
    Beginning MLOps with MLFlow : Deploy Models in AWS SageMaker, Google Cloud, and Microsoft Azure (2020)
    [Erscheinungsort nicht ermittelbar] : Apress | Boston, MA : Safari
    Details
    ISBN: 9781484265499
    Language: English
    Pages: 1 online resource (338 pages)
    Edition: 1st edition
    Keywords: Electronic books ; local ; Electronic books
    Abstract: Integrate MLOps principles into existing or future projects using MLFlow, operationalize your models, and deploy them in AWS SageMaker, Google Cloud, and Microsoft Azure. ​This book guides you through the process of data analysis, model construction, and training. The authors begin by introducing you to basic data analysis on a credit card data set and teach you how to analyze the features and their relationships to the target variable. You will learn how to build logistic regression models in scikit-learn and PySpark, and you will go through the process of hyperparameter tuning with a validation data set. You will explore three different deployment setups of machine learning models with varying levels of automation to help you better understand MLOps. MLFlow is covered and you will explore how to integrate MLOps into your existing code, allowing you to easily track metrics, parameters, graphs, and models. You will be guided through the process of deploying and querying your models with AWS SageMaker, Google Cloud, and Microsoft Azure. And you will learn how to integrate your MLOps setups using Databricks. What You Will Learn Perform basic data analysis and construct models in scikit-learn and PySpark Train, test, and validate your models (hyperparameter tuning) Know what MLOps is and what an ideal MLOps setup looks like Easily integrate MLFlow into your existing or future projects Deploy your models and perform predictions with them on the cloud Who This Book Is For Data scientists and machine learning engineers who want to learn MLOps and know how to operationalize their models
    Note: Online resource; Title from title page (viewed December 7, 2020) , Mode of access: World Wide Web.
    URL: lizenzpflichtig
    Library Location Call Number Volume/Issue/Year Availability
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    MPI Ethno. Forsch.
  • 4
    Online Resource
    Online Resource
    Fundamentals of Apache Flink (2019)
    [Erscheinungsort nicht ermittelbar] : Packt Publishing | Boston, MA : Safari
    Details
    ISBN: 9781789539332
    Language: English
    Pages: 1 online resource (1 video file, approximately 3 hr., 14 min.)
    Edition: 1st edition
    Keywords: Electronic videos ; local
    Abstract: Use Apache Flink and its ecosystem to process real-time big data About This Video Hands-on examples to help you build and administer a Flink ecosystem Gain insights into how all Apache Flink features-such as building your own Flink environment, importing data, and ultimately using it-work Exploit Apache Flink's capabilities (such as distributed data streaming and pipelining) to improve performance In Detail Have you heard of Apache Flink, but don't know how to use it to get on top of big data? Have you used Flink, but want to learn how to set it up and use it properly? Either way, this course is for you. This course first introduces Flink concepts and terminology, and then moves on to building a Flink instance, collecting data, and using that data to generate output that can be used as processed data input into other systems. You will also use the Flink APIs to process data in batch and streaming modes. By the end of the course, you will be capable of using the Apache Flink ecosystem to achieve complex tasks such as event processing and machine learning.
    Note: Online resource; Title from title screen (viewed October 25, 2019)
    URL: lizenzpflichtig
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  • 5
    Online Resource
    Online Resource
    Applied Data Science Using PySpark : Learn the End-to-End Predictive Model-Building Cycle (2020)
    [Erscheinungsort nicht ermittelbar] : Apress | Boston, MA : Safari
    Details
    ISBN: 9781484265000
    Language: English
    Pages: 1 online resource (427 pages)
    Edition: 1st edition
    Keywords: Electronic books ; local ; Electronic books
    Abstract: Discover the capabilities of PySpark and its application in the realm of data science. This comprehensive guide with hand-picked examples of daily use cases will walk you through the end-to-end predictive model-building cycle with the latest techniques and tricks of the trade. Applied Data Science Using PySpark is divided unto six sections which walk you through the book. In section 1, you start with the basics of PySpark focusing on data manipulation. We make you comfortable with the language and then build upon it to introduce you to the mathematical functions available off the shelf. In section 2, you will dive into the art of variable selection where we demonstrate various selection techniques available in PySpark. In section 3, we take you on a journey through machine learning algorithms, implementations, and fine-tuning techniques. We will also talk about different validation metrics and how to use them for picking the best models. Sections 4 and 5 go through machine learning pipelines and various methods available to operationalize the model and serve it through Docker/an API. In the final section, you will cover reusable objects for easy experimentation and learn some tricks that can help you optimize your programs and machine learning pipelines. By the end of this book, you will have seen the flexibility and advantages of PySpark in data science applications. This book is recommended to those who want to unleash the power of parallel computing by simultaneously working with big datasets. What You Will Learn Build an end-to-end predictive model Implement multiple variable selection techniques Operationalize models Master multiple algorithms and implementations Who This Book is For Data scientists and machine learning and deep learning engineers who want to learn and use PySpark for real-time analysis of streaming data.
    Note: Online resource; Title from title page (viewed December 17, 2020) , Mode of access: World Wide Web.
    URL: lizenzpflichtig
    Library Location Call Number Volume/Issue/Year Availability
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  • 6
    Online Resource
    Online Resource
    Big data analytics with Hadoop 3 : build highly effective analytics solutions to gain valuable insight into your big data (2018)
    Birmingham, UK : Packt Publishing
    Details
    ISBN: 9781788624954 , 1788624955
    Language: English
    Pages: 1 online resource (1 volume) , illustrations
    Keywords: Apache Hadoop ; Spark (Electronic resource : Apache Software Foundation) ; Big data ; Information visualization ; Application program interfaces (Computer software) ; Electronic books ; Electronic books ; local
    Abstract: Explore big data concepts, platforms, analytics, and their applications using the power of Hadoop 3 About This Book Learn Hadoop 3 to build effective big data analytics solutions on-premise and on cloud Integrate Hadoop with other big data tools such as R, Python, Apache Spark, and Apache Flink Exploit big data using Hadoop 3 with real-world examples Who This Book Is For Big Data Analytics with Hadoop 3 is for you if you are looking to build high-performance analytics solutions for your enterprise or business using Hadoop 3's powerful features, or you're new to big data analytics. A basic understanding of the Java programming language is required. What You Will Learn Explore the new features of Hadoop 3 along with HDFS, YARN, and MapReduce Get well-versed with the analytical capabilities of Hadoop ecosystem using practical examples Integrate Hadoop with R and Python for more efficient big data processing Learn to use Hadoop with Apache Spark and Apache Flink for real-time data analytics Set up a Hadoop cluster on AWS cloud Perform big data analytics on AWS using Elastic Map Reduce In Detail Apache Hadoop is the most popular platform for big data processing, and can be combined with a host of other big data tools to build powerful analytics solutions. Big Data Analytics with Hadoop 3 shows you how to do just that, by providing insights into the software as well as its benefits with the help of practical examples. Once you have taken a tour of Hadoop 3's latest features, you will get an overview of HDFS, MapReduce, and YARN, and how they enable faster, more efficient big data processing. You will then move on to learning how to integrate Hadoop with the open source tools, such as Python and R, to analyze and visualize data and perform statistical computing on big data. As you get acquainted with all this, you will explore how to use Hadoop 3 with Apache Spark and Apache Flink for real-time data analytics and stream processing. In addition to this, you will understand how to use Hadoop to build analytics solutions on the cloud and an end-to-end pipeline to perform big data analysis using practical use cases. By the end of this book, you will be well-versed with the analytical capabilities of the Hadoop ecosystem. You will be able to build powerful solutions to perform big data analytics and get insight effortlessly. Style and approach Filled with practical examples and use cases, this book will not only help you get up and running with Hadoop, bu...
    Note: Description based on online resource; title from title page (Safari, viewed June 27, 2018)
    URL: https://learning.oreilly.com/library/view/-/9781788628846/?ar
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