Variational Methods For Machine Learning With Applications To Deep Networks
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Variational Methods For Machine Learning With Applications To Deep Networks
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Author : Lucas Pinheiro Cinelli
language : en
Publisher: Springer Nature
Release Date : 2021-05-10
Variational Methods For Machine Learning With Applications To Deep Networks written by Lucas Pinheiro Cinelli and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-05-10 with Technology & Engineering categories.
This book provides a straightforward look at the concepts, algorithms and advantages of Bayesian Deep Learning and Deep Generative Models. Starting from the model-based approach to Machine Learning, the authors motivate Probabilistic Graphical Models and show how Bayesian inference naturally lends itself to this framework. The authors present detailed explanations of the main modern algorithms on variational approximations for Bayesian inference in neural networks. Each algorithm of this selected set develops a distinct aspect of the theory. The book builds from the ground-up well-known deep generative models, such as Variational Autoencoder and subsequent theoretical developments. By also exposing the main issues of the algorithms together with different methods to mitigate such issues, the book supplies the necessary knowledge on generative models for the reader to handle a wide range of data types: sequential or not, continuous or not, labelled or not. The book is self-contained, promptly covering all necessary theory so that the reader does not have to search for additional information elsewhere. Offers a concise self-contained resource, covering the basic concepts to the algorithms for Bayesian Deep Learning; Presents Statistical Inference concepts, offering a set of elucidative examples, practical aspects, and pseudo-codes; Every chapter includes hands-on examples and exercises and a website features lecture slides, additional examples, and other support material.
Variational Methods For Machine Learning With Applications To Deep Networks
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Author : Lucas Pinheiro Cinelli
language : en
Publisher:
Release Date : 2021
Variational Methods For Machine Learning With Applications To Deep Networks written by Lucas Pinheiro Cinelli and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021 with categories.
This book provides a straightforward look at the concepts, algorithms and advantages of Bayesian Deep Learning and Deep Generative Models. Starting from the model-based approach to Machine Learning, the authors motivate Probabilistic Graphical Models and show how Bayesian inference naturally lends itself to this framework. The authors present detailed explanations of the main modern algorithms on variational approximations for Bayesian inference in neural networks. Each algorithm of this selected set develops a distinct aspect of the theory. The book builds from the ground-up well-known deep generative models, such as Variational Autoencoder and subsequent theoretical developments. By also exposing the main issues of the algorithms together with different methods to mitigate such issues, the book supplies the necessary knowledge on generative models for the reader to handle a wide range of data types: sequential or not, continuous or not, labelled or not. The book is self-contained, promptly covering all necessary theory so that the reader does not have to search for additional information elsewhere. Offers a concise self-contained resource, covering the basic concepts to the algorithms for Bayesian Deep Learning; Presents Statistical Inference concepts, offering a set of elucidative examples, practical aspects, and pseudo-codes; Every chapter includes hands-on examples and exercises and a website features lecture slides, additional examples, and other support material.
Generative Ai Techniques Models And Applications
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Author : Rajan Gupta
language : en
Publisher: Springer Nature
Release Date : 2025-03-26
Generative Ai Techniques Models And Applications written by Rajan Gupta and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-03-26 with Computers categories.
This book unlocks the full potential of modern AI systems through a meticulously structured exploration of concepts, techniques, and practical applications. This comprehensive book bridges theoretical foundations with real-world implementations, offering readers a unique perspective on the rapidly evolving field of generative technologies. From computational foundations to ethical considerations, the book systematically covers essential topics including foundation models, large-scale architectures, prompt engineering, and practical applications. The content seamlessly integrates complex technical concepts with industry-relevant examples, making it an invaluable resource for researchers, academicians, and practitioners. Distinguished by its balanced approach to theory and practice, this book serves as both a learning tool and reference guide. Readers will benefit from: Clear explanations of advanced concepts. Practical implementation insights. Current industry applications. Ethical framework discussions. Whether you're conducting research, implementing solutions, or exploring the field, this book provides the knowledge necessary to understand and apply generative AI technologies effectively while considering crucial aspects of security, privacy, and fairness.
Signal Processing And Machine Learning Theory
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Author : Paulo S.R. Diniz
language : en
Publisher: Elsevier
Release Date : 2023-07-10
Signal Processing And Machine Learning Theory written by Paulo S.R. Diniz and has been published by Elsevier this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-07-10 with Technology & Engineering categories.
Signal Processing and Machine Learning Theory, authored by world-leading experts, reviews the principles, methods and techniques of essential and advanced signal processing theory. These theories and tools are the driving engines of many current and emerging research topics and technologies, such as machine learning, autonomous vehicles, the internet of things, future wireless communications, medical imaging, etc. - Provides quick tutorial reviews of important and emerging topics of research in signal processing-based tools - Presents core principles in signal processing theory and shows their applications - Discusses some emerging signal processing tools applied in machine learning methods - References content on core principles, technologies, algorithms and applications - Includes references to journal articles and other literature on which to build further, more specific, and detailed knowledge
Computing And Machine Learning
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Author : Jagdish Chand Bansal
language : en
Publisher: Springer Nature
Release Date : 2024-12-24
Computing And Machine Learning written by Jagdish Chand Bansal and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-12-24 with Computers categories.
This book features high-quality research papers presented at the International Conference on Computing and Machine Learning (CML 2024), organized by Department of Computer Applications, Sikkim Manipal Institute of Technology, Sikkim Manipal University, Sikkim, India during April 29–30, 2024. The book presents diverse range of topics, including machine learning algorithms and models, deep learning and neural networks, computer vision and image processing, natural language processing, robotics and automation, reinforcement learning, big data analytics, cloud computing, internet of things, human-robot interaction, ethical and social implications of AI, applications in healthcare, finance, and industry, computer modeling, quantum computing, high-performance computing, cognitive and parallel computing, cloud computing, distributed computing, embedded computing, human-centered computing and mobile computing.
Prompt Engineering And Generative Ai Applications For Teaching And Learning
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Author : ElSayary, Areej
language : en
Publisher: IGI Global
Release Date : 2025-03-13
Prompt Engineering And Generative Ai Applications For Teaching And Learning written by ElSayary, Areej and has been published by IGI Global this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-03-13 with Computers categories.
By creating specific prompts, educators can harness the power of AI models to generate tailored content, provide instant feedback, and simulate real-world scenarios for deeper learning engagement. Whether it's creating personalized lesson plans, generating creative writing prompts, or assisting with problem-solving exercises, generative AI creates an interactive approach to education. As AI evolves, its potential to support both educators and students in more efficient, adaptive, and inclusive ways may transform the future of learning. Prompt Engineering and Generative AI Applications for Teaching and Learning explores generative AI’s impact on education, navigating the complexities of its integration into teaching and learning strategies. It examines the complex dynamics between AI technology and educational methodologies, offering new perspectives on personalized education, the art of prompt engineering skills, and the role of generative AI in research. This book covers topics such as ethics and law, higher education, and personalized learning, and is a useful resource for academicians, researchers, computer engineers, and data scientists.
Neural Information Processing
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Author : Mufti Mahmud
language : en
Publisher: Springer Nature
Release Date : 2025-08-21
Neural Information Processing written by Mufti Mahmud and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-08-21 with Computers categories.
The sixteen-volume set, CCIS 2282-2297, constitutes the refereed proceedings of the 31st International Conference on Neural Information Processing, ICONIP 2024, held in Auckland, New Zealand, in December 2024. The 472 regular papers presented in this proceedings set were carefully reviewed and selected from 1301 submissions. These papers primarily focus on the following areas: Theory and algorithms; Cognitive neurosciences; Human-centered computing; and Applications.
Deep Learning In Genetics And Genomics
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Author : Khalid Raza
language : en
Publisher: Elsevier
Release Date : 2024-11-28
Deep Learning In Genetics And Genomics written by Khalid Raza and has been published by Elsevier this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-11-28 with Science categories.
Deep Learning in Genetics and Genomics: Vol. 2 (Advanced Applications) delves into the Deep Learning methods and their applications in various fields of studies, including genetics and genomics, bioinformatics, health informatics and medical informatics generating the momentum of today's developments in the field. In 25 chapters this title covers advanced applications in the field which includes deep learning in predictive medicines), analysis of genetic and clinical features, transcriptomics and gene expression patterns analysis, clinical decision support in genetic diagnostics, deep learning in personalised genomics and gene editing, and understanding genetic discoveries through Explainable AI. Further, it also covers various deep learning-based case studies, making this book a unique resource for wider, deeper, and in-depth coverage of recent advancement in deep learning based approaches. This volume is not only a valuable resource for health educators, clinicians, and healthcare professionals but also to graduate students of genetics, genomics, biology, biostatistics, biomedical sciences, bioinformatics, and interdisciplinary sciences. - Embraces the potential that deep learning holds for understanding genome biology - Encourages further advances in this area, extending to all aspects of genomics research - Provides Deep Learning algorithms in genetic and genomic research
Advanced Neural Artificial Intelligence Theories And Applications
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Author : Anna Esposito
language : en
Publisher: Springer Nature
Release Date : 2025-06-24
Advanced Neural Artificial Intelligence Theories And Applications written by Anna Esposito and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-06-24 with Computers categories.
This book illustrates recent advances in Neural Artificial Intelligent Theories and Applications discussed by selected papers presented at 30th edition of the International Workshops on Neural Network (WIRN 2023). The book discusses novel technologies for unsupervised multimodal complex autonomous systems using new generation of AI algorithms. The book also reports on advanced acoustical, perceptual, and psychological analysis of verbal and non-verbal communication of signals originating in spontaneous face-to-face interaction, automatic procedures capable of recognizing human emotional states, and applications improving the performance of human–machine interaction for the deployment of socially and emotionally believable assistive technologies.
Approaches Of Computational Biophysics And Chemistry In Molecular Biology
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Author : Emil Alexov
language : en
Publisher: World Scientific
Release Date : 2025-01-17
Approaches Of Computational Biophysics And Chemistry In Molecular Biology written by Emil Alexov and has been published by World Scientific this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-01-17 with Science categories.
This book covers a broad range of computational biophysics and chemistry methods and their applications to study various phenomena in molecular biology. Highlighting recent advances, it emphasizes enhanced modeling accuracy, longer timescales, and the ability to simulate large biological macromolecules. From molecular dynamics simulations to quantum mechanical methods, the book discusses innovations like polarizable force fields and the integration of machine learning (ML) and artificial intelligence (AI) for improved predictive accuracy. It examines how these techniques predict the pKa of ionizable groups in biological macromolecules such as proteins, DNAs, and RNAs. It is demonstrated that the abovementioned computational techniques can be used to infer the pathogenicity of DNA variants and to reveal the molecular mechanism of diseases.By providing extensive coverage of various methods and diverse applications, this book is a valuable resource that links computational approaches to understanding molecular effects in human diseases, ultimately advancing the field of personalized medicine.