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Deep Learning and XAI Techniques for Anomaly Detection
Create interpretable AI models for transparent and explainable anomaly detection with this hands-on guidePurchase of the print or Kindle book includes a free PDF eBookKey FeaturesBuild auditable XAI models for replicability and regulatory complianceDerive critical insights from transparent anomaly detection modelsStrike the right balance between model accuracy and interpretabilityBook DescriptionDespite 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 forThis 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.
R 1,596.00
Deep Learning with PyTorch Lightning
Build, train, deploy, and scale deep learning models quickly and accurately, improving your productivity using the lightweight PyTorch WrapperKey FeaturesBecome well-versed with PyTorch Lightning architecture and learn how it can be implemented in various industry domainsSpeed up your research using PyTorch Lightning by creating new loss functions, networks, and architecturesTrain and build new algorithms for massive data using distributed trainingBook DescriptionPyTorch Lightning lets researchers build their own Deep Learning (DL) models without having to worry about the boilerplate. With the help of this book, you'll be able to maximize productivity for DL projects while ensuring full flexibility from model formulation through to implementation. You'll take a hands-on approach to implementing PyTorch Lightning models to get up to speed in no time.You'll start by learning how to configure PyTorch Lightning on a cloud platform, understand the architectural components, and explore how they are configured to build various industry solutions. Next, you'll build a network and application from scratch and see how you can expand it based on your specific needs, beyond what the framework can provide. The book also demonstrates how to implement out-of-box capabilities to build and train Self-Supervised Learning, semi-supervised learning, and time series models using PyTorch Lightning. As you advance, you'll discover how generative adversarial networks (GANs) work. Finally, you'll work with deployment-ready applications, focusing on faster performance and scaling, model scoring on massive volumes of data, and model debugging.By the end of this PyTorch book, you'll have developed the knowledge and skills necessary to build and deploy your own scalable DL applications using PyTorch Lightning.What you will learnCustomize models that are built for different datasets, model architectures, and optimizersUnderstand how a variety of Deep Learning models from image recognition and time series to GANs, semi-supervised and self-supervised models can be builtUse out-of-the-box model architectures and pre-trained models using transfer learningRun and tune DL models in a multi-GPU environment using mixed-mode precisionsExplore techniques for model scoring on massive workloadsDiscover troubleshooting techniques while debugging DL modelsWho this book is forThis deep learning book is for citizen data scientists and expert data scientists transitioning from other frameworks to PyTorch Lightning. This book will also be useful for deep learning researchers who are just getting started with coding for deep learning models using PyTorch Lightning. Working knowledge of Python programming and an intermediate-level understanding of statistics and deep learning fundamentals is expected.
R 1,661.00
Algorithmic Probability and Friends. Bayesian Prediction and Artificial Intelligence
Algorithmic probability and friends: Proceedings of the Ray Solomonoff 85th memorial conference is a collection of original work and surveys. The Solomonoff 85th memorial conference was held at Monash University's Clayton campus in Melbourne, Australia as a tribute to pioneer, Ray Solomonoff (1926-2009), honouring his various pioneering works - most particularly, his revolutionary insight in the early 1960s that the universality of Universal Turing Machines (UTMs) could be used for universal Bayesian prediction and artificial intelligence (machine learning). This work continues to increasingly influence and under-pin statistics, econometrics, machine learning, data mining, inductive inference, search algorithms, data compression, theories of (general) intelligence and philosophy of science - and applications of these areas. Ray not only envisioned this as the path to genuine artificial intelligence, but also, still in the 1960s, anticipated stages of progress in machine intelligence which would ultimately lead to machines surpassing human intelligence. Ray warned of the need to anticipate and discuss the potential consequences - and dangers - sooner rather than later. Possibly foremostly, Ray Solomonoff was a fine, happy, frugal and adventurous human being of gentle resolve who managed to fund himself while electing to conduct so much of his paradigm-changing research outside of the university system. The volume contains 35 papers pertaining to the abovementioned topics in tribute to Ray Solomonoff and his legacy.
R 2,200.20
Quantum Computing Explained
A self-contained treatment of the fundamentals of quantum computing This clear, practical book takes quantum computing out of the realm of theoretical physics and teaches the fundamentals of the field to students and professionals who have not had training in quantum computing or quantum information theory, including computer scientists, programmers, electrical engineers, mathematicians, physics students, and chemists. The author cuts through the conventions of typical jargon-laden physics books and instead presents the material through his unique "how-to" approach and friendly, conversational style. Readers will learn how to carry out calculations with explicit details and will gain a fundamental grasp of: * Quantum mechanics * Quantum computation * Teleportation * Quantum cryptography * Entanglement * Quantum algorithms * Error correction A number of worked examples are included so readers can see how quantum computing is done with their own eyes, while answers to similar end-of-chapter problems are provided for readers to check their own work as they learn to master the information. Ideal for professionals and graduate-level students alike, Quantum Computing Explained delivers the fundamentals of quantum computing readers need to be able to understand current research papers and go on to study more advanced quantum texts.
R 4,102.00
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R 916.00
Deep Learning kompakt für Dummies
Wollen Sie sich über Deep Learning informieren und vielleicht erste kleine Schritte machen, diese Technologie zu nutzen? Dann hilft Ihnen dieses Buch. Die Autoren erklären Ihnen, welchen Einfluss Deep Learning im Alltag hat und in welcher Beziehung es zu maschinellem Lernen steht. Sie sammeln erste eigene Erfahrungen mit vorgegebenen Python-Programmen und verstehen so die Funktionsweise von neuronalen Netzen und wie Bilder klassifiziert und Sprache sowie Text verarbeitet werden. So ist dieses Buch ein schneller erster und verständlicher Einstieg in Deep Learning.
R 683.00
Azure Machine Learning Engineering
Fully build and productionize end-to-end machine learning solutions using Azure Machine Learning ServiceKey FeaturesAutomate complete machine learning solutions using Microsoft AzureUnderstand how to productionize machine learning modelsGet to grips with monitoring, MLOps, deep learning, distributed training, and reinforcement learningBook DescriptionData scientists working on productionizing machine learning (ML) workloads face a breadth of challenges at every step owing to the countless factors involved in getting ML models deployed and running. This book offers solutions to common issues, detailed explanations of essential concepts, and step-by-step instructions to productionize ML workloads using the Azure Machine Learning service. You'll see how data scientists and ML engineers working with Microsoft Azure can train and deploy ML models at scale by putting their knowledge to work with this practical guide.Throughout the book, you'll learn how to train, register, and productionize ML models by making use of the power of the Azure Machine Learning service. You'll get to grips with scoring models in real time and batch, explaining models to earn business trust, mitigating model bias, and developing solutions using an MLOps framework.By the end of this Azure Machine Learning book, you'll be ready to build and deploy end-to-end ML solutions into a production system using the Azure Machine Learning service for real-time scenarios.What you will learnTrain ML models in the Azure Machine Learning serviceBuild end-to-end ML pipelinesHost ML models on real-time scoring endpointsMitigate bias in ML modelsGet the hang of using an MLOps framework to productionize modelsSimplify ML model explainability using the Azure Machine Learning service and Azure InterpretWho this book is forMachine learning engineers and data scientists who want to move to ML engineering roles will find this AMLS book useful. Familiarity with the Azure ecosystem will assist with understanding the concepts covered.
R 1,433.00
R 2,801.00
