Deep Learning And Xai Techniques For Anomaly Detection
Download Deep Learning And Xai Techniques For Anomaly Detection full books in PDF, epub, and Kindle. Read online free Deep Learning And Xai Techniques For Anomaly Detection ebook anywhere anytime directly on your device. Fast Download speed and no annoying ads. We cannot guarantee that every ebooks is available!
| Author | : Cher Simon |
| Publisher | : Packt Publishing Ltd |
| Total Pages | : 218 |
| Release | : 2023-01-31 |
| Genre | : Computers |
| ISBN | : 1804613371 |
Create interpretable AI models for transparent and explainable anomaly detection with this hands-on guide Purchase of the print or Kindle book includes a free PDF eBook Key FeaturesBuild auditable XAI models for replicability and regulatory complianceDerive critical insights from transparent anomaly detection modelsStrike the right balance between model accuracy and interpretabilityBook Description Despite promising advances, the opaque nature of deep learning models makes it difficult to interpret them, which is a drawback in terms of their practical deployment and regulatory compliance. Deep Learning and XAI Techniques for Anomaly Detection shows you state-of-the-art methods that'll help you to understand and address these challenges. By leveraging the Explainable AI (XAI) and deep learning techniques described in this book, you'll discover how to successfully extract business-critical insights while ensuring fair and ethical analysis. This practical guide will provide you with tools and best practices to achieve transparency and interpretability with deep learning models, ultimately establishing trust in your anomaly detection applications. Throughout the chapters, you'll get equipped with XAI and anomaly detection knowledge that'll enable you to embark on a series of real-world projects. Whether you are building computer vision, natural language processing, or time series models, you'll learn how to quantify and assess their explainability. By the end of this deep learning book, you'll be able to build a variety of deep learning XAI models and perform validation to assess their explainability. What you will learnExplore deep learning frameworks for anomaly detectionMitigate bias to ensure unbiased and ethical analysisIncrease your privacy and regulatory compliance awarenessBuild deep learning anomaly detectors in several domainsCompare intrinsic and post hoc explainability methodsExamine backpropagation and perturbation methodsConduct model-agnostic and model-specific explainability techniquesEvaluate the explainability of your deep learning modelsWho this book is for This book is for anyone who aspires to explore explainable deep learning anomaly detection, tenured data scientists or ML practitioners looking for Explainable AI (XAI) best practices, or business leaders looking to make decisions on trade-off between performance and interpretability of anomaly detection applications. A basic understanding of deep learning and anomaly detection–related topics using Python is recommended to get the most out of this book.
| Author | : Cher Simon |
| Publisher | : |
| Total Pages | : 0 |
| Release | : 2023 |
| Genre | : Anomaly detection (Computer security) |
| ISBN | : |
Despite promising advances, the opaque nature of deep learning models makes it difficult to interpret them, which is a drawback in terms of their practical deployment and regulatory compliance. Deep Learning and XAI Techniques for Anomaly Detection shows you state-of-the-art methods that'll help you to understand and address these challenges. By leveraging the Explainable AI (XAI) and deep learning techniques described in this book, you'll discover how to successfully extract business-critical insights while ensuring fair and ethical analysis. This practical guide will provide you with tools and best practices to achieve transparency and interpretability with deep learning models, ultimately establishing trust in your anomaly detection applications. Throughout the chapters, you'll get equipped with XAI and anomaly detection knowledge that'll enable you to embark on a series of real-world projects. Whether you are building computer vision, natural language processing, or time series models, you'll learn how to quantify and assess their explainability. By the end of this deep learning book, you'll be able to build a variety of deep learning XAI models and perform validation to assess their explainability.
| Author | : Sridhar Alla |
| Publisher | : Apress |
| Total Pages | : 427 |
| Release | : 2019-10-10 |
| Genre | : Computers |
| ISBN | : 1484251776 |
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 LearnUnderstand 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
| Author | : Denis Rothman |
| Publisher | : Packt Publishing Ltd |
| Total Pages | : 455 |
| Release | : 2020-07-31 |
| Genre | : Computers |
| ISBN | : 1800202768 |
Resolve the black box models in your AI applications to make them fair, trustworthy, and secure. Familiarize yourself with the basic principles and tools to deploy Explainable AI (XAI) into your apps and reporting interfaces. Key FeaturesLearn explainable AI tools and techniques to process trustworthy AI resultsUnderstand how to detect, handle, and avoid common issues with AI ethics and biasIntegrate fair AI into popular apps and reporting tools to deliver business value using Python and associated toolsBook Description Effectively translating AI insights to business stakeholders requires careful planning, design, and visualization choices. Describing the problem, the model, and the relationships among variables and their findings are often subtle, surprising, and technically complex. Hands-On Explainable AI (XAI) with Python will see you work with specific hands-on machine learning Python projects that are strategically arranged to enhance your grasp on AI results analysis. You will be building models, interpreting results with visualizations, and integrating XAI reporting tools and different applications. You will build XAI solutions in Python, TensorFlow 2, Google Cloud’s XAI platform, Google Colaboratory, and other frameworks to open up the black box of machine learning models. The book will introduce you to several open-source XAI tools for Python that can be used throughout the machine learning project life cycle. You will learn how to explore machine learning model results, review key influencing variables and variable relationships, detect and handle bias and ethics issues, and integrate predictions using Python along with supporting the visualization of machine learning models into user explainable interfaces. By the end of this AI book, you will possess an in-depth understanding of the core concepts of XAI. What you will learnPlan for XAI through the different stages of the machine learning life cycleEstimate the strengths and weaknesses of popular open-source XAI applicationsExamine how to detect and handle bias issues in machine learning dataReview ethics considerations and tools to address common problems in machine learning dataShare XAI design and visualization best practicesIntegrate explainable AI results using Python modelsUse XAI toolkits for Python in machine learning life cycles to solve business problemsWho this book is for This book is not an introduction to Python programming or machine learning concepts. You must have some foundational knowledge and/or experience with machine learning libraries such as scikit-learn to make the most out of this book. Some of the potential readers of this book include: Professionals who already use Python for as data science, machine learning, research, and analysisData analysts and data scientists who want an introduction into explainable AI tools and techniquesAI Project managers who must face the contractual and legal obligations of AI Explainability for the acceptance phase of their applications
| Author | : Van Vung Pham |
| Publisher | : Packt Publishing Ltd |
| Total Pages | : 318 |
| Release | : 2023-04-14 |
| Genre | : Computers |
| ISBN | : 1800566603 |
Explore Detectron2 using cutting-edge models and learn all about implementing future computer vision applications in custom domains Purchase of the print or Kindle book includes a free PDF eBook Key Features Learn how to tackle common computer vision tasks in modern businesses with Detectron2 Leverage Detectron2 performance tuning techniques to control the model's finest details Deploy Detectron2 models into production and develop Detectron2 models for mobile devices Book Description Computer vision is a crucial component of many modern businesses, including automobiles, robotics, and manufacturing, and its market is growing rapidly. This book helps you explore Detectron2, Facebook's next-gen library providing cutting-edge detection and segmentation algorithms. It's used in research and practical projects at Facebook to support computer vision tasks, and its models can be exported to TorchScript or ONNX for deployment. The book provides you with step-by-step guidance on using existing models in Detectron2 for computer vision tasks (object detection, instance segmentation, key-point detection, semantic detection, and panoptic segmentation). You'll get to grips with the theories and visualizations of Detectron2's architecture and learn how each module in Detectron2 works. As you advance, you'll build your practical skills by working on two real-life projects (preparing data, training models, fine-tuning models, and deployments) for object detection and instance segmentation tasks using Detectron2. Finally, you'll deploy Detectron2 models into production and develop Detectron2 applications for mobile devices. By the end of this deep learning book, you'll have gained sound theoretical knowledge and useful hands-on skills to help you solve advanced computer vision tasks using Detectron2. What you will learn Build computer vision applications using existing models in Detectron2 Grasp the concepts underlying Detectron2's architecture and components Develop real-life projects for object detection and object segmentation using Detectron2 Improve model accuracy using Detectron2's performance-tuning techniques Deploy Detectron2 models into server environments with ease Develop and deploy Detectron2 models into browser and mobile environments Who this book is for If you are a deep learning application developer, researcher, or software developer with some prior knowledge about deep learning, this book is for you to get started and develop deep learning models for computer vision applications. Even if you are an expert in computer vision and curious about the features of Detectron2, or you would like to learn some cutting-edge deep learning design patterns, you will find this book helpful. Some HTML, Android, and C++ programming skills are advantageous if you want to deploy computer vision applications using these platforms.
| Author | : Mohammad Amir Khusru Akhtar |
| Publisher | : Springer Nature |
| Total Pages | : 381 |
| Release | : |
| Genre | : |
| ISBN | : 3031664892 |
| Author | : Jermsittiparsert, Kittisak |
| Publisher | : IGI Global |
| Total Pages | : 486 |
| Release | : 2024-09-18 |
| Genre | : Computers |
| ISBN | : |
In mental health care, artificial intelligence (AI) tools can enhance diagnostic accuracy, personalize treatment plans, and provide support through virtual therapy and chatbots that offer real-time assistance. These technologies can help identify early signs of mental health issues by analyzing patterns in speech, behavior, and physiological data. However, the integration of AI also raises concerns about privacy, data security, and the potential for algorithmic bias, which could impact quality of care. As AI continues to evolve, its role in psychological well-being and healthcare will depend on addressing these ethical and practical considerations while harnessing its potential to improve mental health outcomes and streamline healthcare delivery. AI Technologies and Advancements for Psychological Well-Being and Healthcare discusses the latest innovations in AI that are transforming the landscape of mental health and healthcare services. This book explores how AI applications, such as machine learning algorithms and natural language processing, are enhancing diagnostic accuracy, personalizing treatment options, and improving patient outcomes. Covering topics such as behavioral artificial intelligence, medical diagnosis, and precision medicine, this book is an excellent resource for mental health professionals, healthcare providers and administrators, AI and data scientists, academicians, researchers, healthcare policymakers, and more.
| Author | : Andrey Sadovykh |
| Publisher | : Springer Nature |
| Total Pages | : 329 |
| Release | : |
| Genre | : |
| ISBN | : 3031422120 |
| Author | : Kim Phuc Tran |
| Publisher | : Springer Nature |
| Total Pages | : 202 |
| Release | : |
| Genre | : |
| ISBN | : 3031714954 |
| Author | : Luca Longo |
| Publisher | : Springer Nature |
| Total Pages | : 471 |
| Release | : |
| Genre | : |
| ISBN | : 303163800X |
