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Auteur Amita KAPOOR |
Documents disponibles écrits par cet auteur (2)
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Platform and Model Design for Responsible AI : Design and build resilient, private, fair, and transparent machine learning models / Amita KAPOOR / PACKT PUBLISHING (2023)
Titre : Platform and Model Design for Responsible AI : Design and build resilient, private, fair, and transparent machine learning models Type de document : e-book Auteurs : Amita KAPOOR Editeur : PACKT PUBLISHING Année de publication : 2023 ISBN/ISSN/EAN : 9781803237077 Note générale : copyrighted Langues : Anglais (eng) Résumé : Craft ethical AI projects with privacy, fairness, and risk assessment features for scalable and distributed systems while maintaining explainability and sustainability Purchase of the print or Kindle book includes a free PDF eBookKey FeaturesLearn risk assessment for machine learning frameworks in a global landscapeDiscover patterns for next-generation AI ecosystems for successful product designMake explainable predictions for privacy and fairness-enabled ML trainingBook DescriptionAI algorithms are ubiquitous and used for tasks, from recruiting to deciding who will get a loan. With such widespread use of AI in the decision-making process, it’s necessary to build an explainable, responsible, transparent, and trustworthy AI-enabled system. With Platform and Model Design for Responsible AI, you’ll be able to make existing black box models transparent. You’ll be able to identify and eliminate bias in your models, deal with uncertainty arising from both data and model limitations, and provide a responsible AI solution. You’ll start by designing ethical models for traditional and deep learning ML models, as well as deploying them in a sustainable production setup. After that, you’ll learn how to set up data pipelines, validate datasets, and set up component microservices in a secure and private way in any cloud-agnostic framework. You’ll then build a fair and private ML model with proper constraints, tune the hyperparameters, and evaluate the model metrics. By the end of this book, you’ll know the best practices to comply with data privacy and ethics laws, in addition to the techniques needed for data anonymization. You’ll be able to develop models with explainability, store them in feature stores, and handle uncertainty in model predictions.What you will learnUnderstand the threats and risks involved in ML modelsDiscover varying levels of risk mitigation strategies and risk tiering toolsApply traditional and deep learning optimization techniques efficientlyBuild auditable and interpretable ML models and feature storesUnderstand the concept of uncertainty and explore model explainability toolsDevelop models for different clouds including AWS, Azure, and GCPExplore ML orchestration tools such as Kubeflow and Vertex AIIncorporate privacy and fairness in ML models from design to deploymentWho this book is forThis book is for experienced machine learning professionals looking to understand the risks and leakages of ML models and frameworks, and learn to develop and use reusable components to reduce effort and cost in setting up and maintaining the AI ecosystem. Nombre d'accès : Illimité En ligne : http://library.ez.neoma-bs.fr/login?url=https://www.scholarvox.com/book/88946445 Permalink : https://cataloguelibrary.neoma-bs.fr/index.php?lvl=notice_display&id=579843 Deep Learning with TensorFlow and Keras : Build and deploy supervised, unsupervised, deep, and reinforcement learning models / Amita KAPOOR / PACKT PUBLISHING (2022)
Titre : Deep Learning with TensorFlow and Keras : Build and deploy supervised, unsupervised, deep, and reinforcement learning models Type de document : e-book Auteurs : Amita KAPOOR Editeur : PACKT PUBLISHING Année de publication : 2022 ISBN/ISSN/EAN : 9781803232911 Note générale : copyrighted Langues : Anglais (eng) Résumé : Build cutting edge machine and deep learning systems for the lab, production, and mobile devicesKey FeaturesUnderstand the fundamentals of deep learning and machine learning through clear explanations and extensive code samplesImplement graph neural networks, transformers using Hugging Face and TensorFlow Hub, and joint and contrastive learningLearn cutting-edge machine and deep learning techniquesBook DescriptionDeep Learning with TensorFlow and Keras teaches you neural networks and deep learning techniques using TensorFlow (TF) and Keras. You'll learn how to write deep learning applications in the most powerful, popular, and scalable machine learning stack available. TensorFlow 2.x focuses on simplicity and ease of use, with updates like eager execution, intuitive higher-level APIs based on Keras, and flexible model building on any platform. This book uses the latest TF 2.0 features and libraries to present an overview of supervised and unsupervised machine learning models and provides a comprehensive analysis of deep learning and reinforcement learning models using practical examples for the cloud, mobile, and large production environments. This book also shows you how to create neural networks with TensorFlow, runs through popular algorithms (regression, convolutional neural networks (CNNs), transformers, generative adversarial networks (GANs), recurrent neural networks (RNNs), natural language processing (NLP), and graph neural networks (GNNs)), covers working example apps, and then dives into TF in production, TF mobile, and TensorFlow with AutoML.What you will learnLearn how to use the popular GNNs with TensorFlow to carry out graph mining tasksDiscover the world of transformers, from pretraining to fine-tuning to evaluating themApply self-supervised learning to natural language processing, computer vision, and audio signal processingCombine probabilistic and deep learning models using TensorFlow ProbabilityTrain your models on the cloud and put TF to work in real environmentsBuild machine learning and deep learning systems with TensorFlow 2.x and the Keras APIWho this book is forThis hands-on machine learning book is for Python developers and data scientists who want to build machine learning and deep learning systems with TensorFlow. This book gives you the theory and practice required to use Keras, TensorFlow, and AutoML to build machine learning systems.Some machine learning knowledge would be useful. We don't assume TF knowledge. Nombre d'accès : Illimité En ligne : http://library.ez.neoma-bs.fr/login?url=https://www.scholarvox.com/book/88936120 Permalink : https://cataloguelibrary.neoma-bs.fr/index.php?lvl=notice_display&id=558362
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