Learning Deep Generative Models

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Generative Deep Learning

Author : David Foster
Publisher : "O'Reilly Media, Inc."
ISBN 13 : 1492041890
Total Pages : 301 pages
Book Rating : 4.4/5 (92 download)

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Book Synopsis Generative Deep Learning by : David Foster

Download or read book Generative Deep Learning written by David Foster and published by "O'Reilly Media, Inc.". This book was released on 2019-06-28 with total page 301 pages. Available in PDF, EPUB and Kindle. Book excerpt: Generative modeling is one of the hottest topics in AI. It’s now possible to teach a machine to excel at human endeavors such as painting, writing, and composing music. With this practical book, machine-learning engineers and data scientists will discover how to re-create some of the most impressive examples of generative deep learning models, such as variational autoencoders,generative adversarial networks (GANs), encoder-decoder models and world models. Author David Foster demonstrates the inner workings of each technique, starting with the basics of deep learning before advancing to some of the most cutting-edge algorithms in the field. Through tips and tricks, you’ll understand how to make your models learn more efficiently and become more creative. Discover how variational autoencoders can change facial expressions in photos Build practical GAN examples from scratch, including CycleGAN for style transfer and MuseGAN for music generation Create recurrent generative models for text generation and learn how to improve the models using attention Understand how generative models can help agents to accomplish tasks within a reinforcement learning setting Explore the architecture of the Transformer (BERT, GPT-2) and image generation models such as ProGAN and StyleGAN

Deep Learning

Author : Ian Goodfellow
Publisher : MIT Press
ISBN 13 : 0262337371
Total Pages : 801 pages
Book Rating : 4.2/5 (623 download)

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Book Synopsis Deep Learning by : Ian Goodfellow

Download or read book Deep Learning written by Ian Goodfellow and published by MIT Press. This book was released on 2016-11-10 with total page 801 pages. Available in PDF, EPUB and Kindle. Book excerpt: An introduction to a broad range of topics in deep learning, covering mathematical and conceptual background, deep learning techniques used in industry, and research perspectives. “Written by three experts in the field, Deep Learning is the only comprehensive book on the subject.” —Elon Musk, cochair of OpenAI; cofounder and CEO of Tesla and SpaceX Deep learning is a form of machine learning that enables computers to learn from experience and understand the world in terms of a hierarchy of concepts. Because the computer gathers knowledge from experience, there is no need for a human computer operator to formally specify all the knowledge that the computer needs. The hierarchy of concepts allows the computer to learn complicated concepts by building them out of simpler ones; a graph of these hierarchies would be many layers deep. This book introduces a broad range of topics in deep learning. The text offers mathematical and conceptual background, covering relevant concepts in linear algebra, probability theory and information theory, numerical computation, and machine learning. It describes deep learning techniques used by practitioners in industry, including deep feedforward networks, regularization, optimization algorithms, convolutional networks, sequence modeling, and practical methodology; and it surveys such applications as natural language processing, speech recognition, computer vision, online recommendation systems, bioinformatics, and videogames. Finally, the book offers research perspectives, covering such theoretical topics as linear factor models, autoencoders, representation learning, structured probabilistic models, Monte Carlo methods, the partition function, approximate inference, and deep generative models. Deep Learning can be used by undergraduate or graduate students planning careers in either industry or research, and by software engineers who want to begin using deep learning in their products or platforms. A website offers supplementary material for both readers and instructors.

Deep Generative Modeling

Author : Jakub M. Tomczak
Publisher : Springer Nature
ISBN 13 : 303164087X
Total Pages : 325 pages
Book Rating : 4.0/5 (316 download)

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Book Synopsis Deep Generative Modeling by : Jakub M. Tomczak

Download or read book Deep Generative Modeling written by Jakub M. Tomczak and published by Springer Nature. This book was released on with total page 325 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Deep Generative Models, and Data Augmentation, Labelling, and Imperfections

Author : Sandy Engelhardt
Publisher : Springer Nature
ISBN 13 : 3030882101
Total Pages : 278 pages
Book Rating : 4.0/5 (38 download)

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Book Synopsis Deep Generative Models, and Data Augmentation, Labelling, and Imperfections by : Sandy Engelhardt

Download or read book Deep Generative Models, and Data Augmentation, Labelling, and Imperfections written by Sandy Engelhardt and published by Springer Nature. This book was released on 2021-09-29 with total page 278 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book constitutes the refereed proceedings of the First MICCAI Workshop on Deep Generative Models, DG4MICCAI 2021, and the First MICCAI Workshop on Data Augmentation, Labelling, and Imperfections, DALI 2021, held in conjunction with MICCAI 2021, in October 2021. The workshops were planned to take place in Strasbourg, France, but were held virtually due to the COVID-19 pandemic. DG4MICCAI 2021 accepted 12 papers from the 17 submissions received. The workshop focusses on recent algorithmic developments, new results, and promising future directions in Deep Generative Models. Deep generative models such as Generative Adversarial Network (GAN) and Variational Auto-Encoder (VAE) are currently receiving widespread attention from not only the computer vision and machine learning communities, but also in the MIC and CAI community. For DALI 2021, 15 papers from 32 submissions were accepted for publication. They focus on rigorous study of medical data related to machine learning systems.

Large-scale Kernel Machines

Author : Léon Bottou
Publisher : MIT Press
ISBN 13 : 0262026252
Total Pages : 409 pages
Book Rating : 4.2/5 (62 download)

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Book Synopsis Large-scale Kernel Machines by : Léon Bottou

Download or read book Large-scale Kernel Machines written by Léon Bottou and published by MIT Press. This book was released on 2007 with total page 409 pages. Available in PDF, EPUB and Kindle. Book excerpt: Solutions for learning from large scale datasets, including kernel learning algorithms that scale linearly with the volume of the data and experiments carried out on realistically large datasets. Pervasive and networked computers have dramatically reduced the cost of collecting and distributing large datasets. In this context, machine learning algorithms that scale poorly could simply become irrelevant. We need learning algorithms that scale linearly with the volume of the data while maintaining enough statistical efficiency to outperform algorithms that simply process a random subset of the data. This volume offers researchers and engineers practical solutions for learning from large scale datasets, with detailed descriptions of algorithms and experiments carried out on realistically large datasets. At the same time it offers researchers information that can address the relative lack of theoretical grounding for many useful algorithms. After a detailed description of state-of-the-art support vector machine technology, an introduction of the essential concepts discussed in the volume, and a comparison of primal and dual optimization techniques, the book progresses from well-understood techniques to more novel and controversial approaches. Many contributors have made their code and data available online for further experimentation. Topics covered include fast implementations of known algorithms, approximations that are amenable to theoretical guarantees, and algorithms that perform well in practice but are difficult to analyze theoretically. Contributors Léon Bottou, Yoshua Bengio, Stéphane Canu, Eric Cosatto, Olivier Chapelle, Ronan Collobert, Dennis DeCoste, Ramani Duraiswami, Igor Durdanovic, Hans-Peter Graf, Arthur Gretton, Patrick Haffner, Stefanie Jegelka, Stephan Kanthak, S. Sathiya Keerthi, Yann LeCun, Chih-Jen Lin, Gaëlle Loosli, Joaquin Quiñonero-Candela, Carl Edward Rasmussen, Gunnar Rätsch, Vikas Chandrakant Raykar, Konrad Rieck, Vikas Sindhwani, Fabian Sinz, Sören Sonnenburg, Jason Weston, Christopher K. I. Williams, Elad Yom-Tov

Generative Adversarial Networks with Python

Author : Jason Brownlee
Publisher : Machine Learning Mastery
ISBN 13 :
Total Pages : 655 pages
Book Rating : 4./5 ( download)

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Book Synopsis Generative Adversarial Networks with Python by : Jason Brownlee

Download or read book Generative Adversarial Networks with Python written by Jason Brownlee and published by Machine Learning Mastery. This book was released on 2019-07-11 with total page 655 pages. Available in PDF, EPUB and Kindle. Book excerpt: Step-by-step tutorials on generative adversarial networks in python for image synthesis and image translation.

Deep Generative Models for Images, Texts, and Graphs

Author : Lei Cai
Publisher :
ISBN 13 :
Total Pages : 170 pages
Book Rating : 4.6/5 (781 download)

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Book Synopsis Deep Generative Models for Images, Texts, and Graphs by : Lei Cai

Download or read book Deep Generative Models for Images, Texts, and Graphs written by Lei Cai and published by . This book was released on 2020 with total page 170 pages. Available in PDF, EPUB and Kindle. Book excerpt: Generative models have become a powerful unsupervised learning method that can learn the distribution from existing data and generate new samples following the same distribution. With the advances of deep learning, deep generative models have shown promising performance in learning the distribution of complex data. Deep generative models have been applied to a wide range of applications, including image, text, and graph generation tasks. In most real-world applications, data are represented as images, texts, and graphs. This dissertation develops novel, deep generative models toward solving efficiency and accuracy problems in image, text, and graph analysis tasks. More specifically, these deep generative models are designed for improving three tasks; those are multi-modality missing data completion for Alzheimer's disease diagnosis, dialogue generation, and graph link prediction. I mainly focus on generating high-quality samples to facilitate the data analyses in these tasks. The meaning of high quality samples is specific to each different task. In missing data completion tasks, clear and informative images are required for disease diagnosis. In dialogue generation, diverse and reasonable responses for the given dialogue context are critical for improved communication. In graph link prediction, generating potential or missing links in the network with high confidence leads to greater prediction accuracy.In this dissertation, I analyze the limitations and drawbacks of existing models and methods for solving the three tasks. To overcome these limitations, the following four deep generative models are proposed for the tasks. An encoder-decoder network optimized by a combination of three loss functions is developed to generative clear and informative images. A conditional dialogue generation model associated with novel discriminator networks is proposed to generate diverse and reasonable responses. A multi-scale link prediction framework that employs a new node aggregation method to transform the graph into different scales is designed to perform link prediction. A line graph neural networks model, where the original graph is transformed into a corresponding line graph to enable efficient feature learning for the target link, is developed for link prediction tasks. Experimental results demonstrate that the proposed models are able to achieve promising, improved performance on image, text, and graph tasks.

Graph Representation Learning

Author : William L. William L. Hamilton
Publisher : Springer Nature
ISBN 13 : 3031015886
Total Pages : 141 pages
Book Rating : 4.0/5 (31 download)

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Book Synopsis Graph Representation Learning by : William L. William L. Hamilton

Download or read book Graph Representation Learning written by William L. William L. Hamilton and published by Springer Nature. This book was released on 2022-06-01 with total page 141 pages. Available in PDF, EPUB and Kindle. Book excerpt: Graph-structured data is ubiquitous throughout the natural and social sciences, from telecommunication networks to quantum chemistry. Building relational inductive biases into deep learning architectures is crucial for creating systems that can learn, reason, and generalize from this kind of data. Recent years have seen a surge in research on graph representation learning, including techniques for deep graph embeddings, generalizations of convolutional neural networks to graph-structured data, and neural message-passing approaches inspired by belief propagation. These advances in graph representation learning have led to new state-of-the-art results in numerous domains, including chemical synthesis, 3D vision, recommender systems, question answering, and social network analysis. This book provides a synthesis and overview of graph representation learning. It begins with a discussion of the goals of graph representation learning as well as key methodological foundations in graph theory and network analysis. Following this, the book introduces and reviews methods for learning node embeddings, including random-walk-based methods and applications to knowledge graphs. It then provides a technical synthesis and introduction to the highly successful graph neural network (GNN) formalism, which has become a dominant and fast-growing paradigm for deep learning with graph data. The book concludes with a synthesis of recent advancements in deep generative models for graphs—a nascent but quickly growing subset of graph representation learning.

On the Evaluation of Deep Generative Models

Author : Sharon Zhou
Publisher :
ISBN 13 :
Total Pages : pages
Book Rating : 4.:/5 (125 download)

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Book Synopsis On the Evaluation of Deep Generative Models by : Sharon Zhou

Download or read book On the Evaluation of Deep Generative Models written by Sharon Zhou and published by . This book was released on 2021 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: Evaluation drives and tracks progress in every field. Metrics of evaluation are designed to assess important criteria in an area, and aid us in understanding the quantitative differences between one breakthrough and another. In machine learning, evaluation metrics have historically acted as north stars towards which researchers have optimized and organized their methods and findings. While evaluation metrics have been straightforward to construct and implement in some subfields of machine learning, they have been notoriously difficult to design in generative models. Several reasons emerge to explain this: (1) there are no gold standard outputs to compare against, unlike held-out test sets, (2) because of their diverse training methods and formulations, inherent model properties are difficult to measure consistently, and sampled outputs are often used for evaluation instead, (3) dependence on external (pretrained) models that add another layer of bias and uncertainty, and (4) inconsistent results without a large number of samples. As a result, generative models have suffered from noisy assessments that occupy a changing evaluation landscape, in contrast to the relative stability of their discriminative counterparts. In this manuscript, we examine several important criteria for generative models and introduce evaluation metrics to address each one while discussing the aforementioned issues in generative model evaluation. In particular, we examine the challenge of measuring the perceptual realism of generated outputs and introduce a human-in-the-loop evaluation system that leverages psychophysics theory to ground the method in human perception literature and crowdsourcing techniques to construct an efficient, reliable, and consistent method for comparing different models. In addition to this, we analyze disentanglement, an increasingly important property for assessing learned representations, by measuring an intrinsic property of a generative model's data manifold using persistent homology. The final work in this manuscript takes a step towards assessing a generative model and its different modes with a key application in mind, specifically the stylistic fidelity across different generated modes in a multimodal setting.

Deep Generative Models

Author : Anirban Mukhopadhyay
Publisher : Springer Nature
ISBN 13 : 3031727444
Total Pages : 235 pages
Book Rating : 4.0/5 (317 download)

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Book Synopsis Deep Generative Models by : Anirban Mukhopadhyay

Download or read book Deep Generative Models written by Anirban Mukhopadhyay and published by Springer Nature. This book was released on with total page 235 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Variational Methods for Machine Learning with Applications to Deep Networks

Author : Lucas Pinheiro Cinelli
Publisher : Springer Nature
ISBN 13 : 3030706796
Total Pages : 173 pages
Book Rating : 4.0/5 (37 download)

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Book Synopsis Variational Methods for Machine Learning with Applications to Deep Networks by : Lucas Pinheiro Cinelli

Download or read book Variational Methods for Machine Learning with Applications to Deep Networks written by Lucas Pinheiro Cinelli and published by Springer Nature. This book was released on 2021-05-10 with total page 173 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Learning Deep Generative Models

Author : Ruslan Salakhutdinov
Publisher :
ISBN 13 : 9780494610800
Total Pages : 196 pages
Book Rating : 4.6/5 (18 download)

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Book Synopsis Learning Deep Generative Models by : Ruslan Salakhutdinov

Download or read book Learning Deep Generative Models written by Ruslan Salakhutdinov and published by . This book was released on 2009 with total page 196 pages. Available in PDF, EPUB and Kindle. Book excerpt: Building intelligent systems that are capable of extracting high-level representations from high-dimensional sensory data lies at the core of solving many AI related tasks, including object recognition, speech perception, and language understanding. Theoretical and biological arguments strongly suggest that building such systems requires models with deep architectures that involve many layers of nonlinear processing. The aim of the thesis is to demonstrate that deep generative models that contain many layers of latent variables and millions of parameters can be learned efficiently, and that the learned high-level feature representations can be successfully applied in a wide spectrum of application domains, including visual object recognition, information retrieval, and classification and regression tasks. In addition, similar methods can be used for nonlinear dimensionality reduction. The first part of the thesis focuses on analysis and applications of probabilistic generative models called Deep Belief Networks. We show that these deep hierarchical models can learn useful feature representations from a large supply of unlabeled sensory inputs. The learned high-level representations capture a lot of structure in the input data, which is useful for subsequent problem-specific tasks, such as classification, regression or information retrieval, even though these tasks are unknown when the generative model is being trained. In the second part of the thesis, we introduce a new learning algorithm for a different type of hierarchical probabilistic model, which we call a Deep Boltzmann Machine. Like Deep Belief Networks, Deep Boltzmann Machines have the potential of learning internal representations that become increasingly complex at higher layers, which is a promising way of solving object and speech recognition problems. Unlike Deep Belief Networks and many existing models with deep architectures, the approximate inference procedure, in addition to a fast bottom-up pass, can incorporate top-down feedback. This allows Deep Boltzmann Machines to better propagate uncertainty about ambiguous inputs.

Deep Learning Illustrated

Author : Jon Krohn
Publisher : Addison-Wesley Professional
ISBN 13 : 0135121728
Total Pages : 725 pages
Book Rating : 4.1/5 (351 download)

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Book Synopsis Deep Learning Illustrated by : Jon Krohn

Download or read book Deep Learning Illustrated written by Jon Krohn and published by Addison-Wesley Professional. This book was released on 2019-08-05 with total page 725 pages. Available in PDF, EPUB and Kindle. Book excerpt: "The authors’ clear visual style provides a comprehensive look at what’s currently possible with artificial neural networks as well as a glimpse of the magic that’s to come." – Tim Urban, author of Wait But Why Fully Practical, Insightful Guide to Modern Deep Learning Deep learning is transforming software, facilitating powerful new artificial intelligence capabilities, and driving unprecedented algorithm performance. Deep Learning Illustrated is uniquely intuitive and offers a complete introduction to the discipline’s techniques. Packed with full-color figures and easy-to-follow code, it sweeps away the complexity of building deep learning models, making the subject approachable and fun to learn. World-class instructor and practitioner Jon Krohn–with visionary content from Grant Beyleveld and beautiful illustrations by Aglaé Bassens–presents straightforward analogies to explain what deep learning is, why it has become so popular, and how it relates to other machine learning approaches. Krohn has created a practical reference and tutorial for developers, data scientists, researchers, analysts, and students who want to start applying it. He illuminates theory with hands-on Python code in accompanying Jupyter notebooks. To help you progress quickly, he focuses on the versatile deep learning library Keras to nimbly construct efficient TensorFlow models; PyTorch, the leading alternative library, is also covered. You’ll gain a pragmatic understanding of all major deep learning approaches and their uses in applications ranging from machine vision and natural language processing to image generation and game-playing algorithms. Discover what makes deep learning systems unique, and the implications for practitioners Explore new tools that make deep learning models easier to build, use, and improve Master essential theory: artificial neurons, training, optimization, convolutional nets, recurrent nets, generative adversarial networks (GANs), deep reinforcement learning, and more Walk through building interactive deep learning applications, and move forward with your own artificial intelligence projects Register your book for convenient access to downloads, updates, and/or corrections as they become available. See inside book for details.

De novo Molecular Design

Author : Gisbert Schneider
Publisher : Wiley-VCH
ISBN 13 : 9783527334612
Total Pages : 0 pages
Book Rating : 4.3/5 (346 download)

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Book Synopsis De novo Molecular Design by : Gisbert Schneider

Download or read book De novo Molecular Design written by Gisbert Schneider and published by Wiley-VCH. This book was released on 2013-12-23 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Systematically examining current methods and strategies, this ready reference covers a wide range of molecular structures, from organic-chemical drugs to peptides, Proteins and nucleic acids, in line with emerging new drug classes derived from biomacromolecules. A leader in the field and one of the pioneers of this young discipline has assembled here the most prominent experts from across the world to provide first-hand knowledge. While most of their methods and examples come from the area of pharmaceutical discovery and development, the approaches are equally applicable for chemical probes and diagnostics, pesticides, and any other molecule designed to interact with a biological system. Numerous images and screenshots illustrate the many examples and method descriptions. With its broad and balanced coverage, this will be the firststop resource not only for medicinal chemists, biochemists and biotechnologists, but equally for bioinformaticians and molecular designers for many years to come. From the content: * Reaction-driven de novo design * Adaptive methods in molecular design * Design of ligands against multitarget profiles * Free energy methods in ligand design * Fragment-based de novo design * Automated design of focused and target family-oriented compound libraries * Molecular de novo design by nature-inspired computing * 3D QSAR approaches to de novo drug design * Bioisosteres in de novo design * De novo design of peptides, proteins and nucleic acid structures, including RNA aptamers and many more.

Deep Generative Models and Applications

Author : Tatjana Chavdarova
Publisher :
ISBN 13 :
Total Pages : 149 pages
Book Rating : 4.:/5 (119 download)

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Book Synopsis Deep Generative Models and Applications by : Tatjana Chavdarova

Download or read book Deep Generative Models and Applications written by Tatjana Chavdarova and published by . This book was released on 2020 with total page 149 pages. Available in PDF, EPUB and Kindle. Book excerpt: Mots-clés de l'auteur: Deep Learning ; Deep Generative Models ; Generative Adversarial Networks ; Variance Reduced Gradient.

Advances in Deep Generative Models for Medical Artificial Intelligence

Author : Hazrat Ali
Publisher : Springer Nature
ISBN 13 : 3031463412
Total Pages : 259 pages
Book Rating : 4.0/5 (314 download)

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Book Synopsis Advances in Deep Generative Models for Medical Artificial Intelligence by : Hazrat Ali

Download or read book Advances in Deep Generative Models for Medical Artificial Intelligence written by Hazrat Ali and published by Springer Nature. This book was released on 2023-12-16 with total page 259 pages. Available in PDF, EPUB and Kindle. Book excerpt: Generative Artificial Intelligence is rapidly advancing with many state-of-the-art performances on computer vision, speech processing, and natural language processing tasks. Generative adversarial networks and neural diffusion models can generate high-quality synthetic images of human faces, artworks, and coherent essays on different topics. Generative models are also transforming Medical Artificial Intelligence, given their potential to learn complex features from medical imaging and healthcare data. Hence, computer-aided diagnosis and healthcare are benefiting from Medical Artificial Intelligence and Generative Artificial Intelligence. This book presents the recent advances in generative models for Medical Artificial Intelligence. It covers many applications of generative models for medical image data, including volumetric medical image segmentation, data augmentation, MRI reconstruction, and modeling of spatiotemporal medical data. This book highlights the recent advancements in Generative Artificial Intelligence for medical and healthcare applications, using medical imaging and clinical and electronic health records data. Furthermore, the book comprehensively presents the concepts and applications of deep learning-based artificial intelligence methods, such as generative adversarial networks, convolutional neural networks, and vision transformers. It also presents a quantitative and qualitative analysis of data augmentation and synthesis performances of Generative Artificial Intelligence models. This book is the result of the collaborative efforts and hard work of many minds who contributed to it and illuminated the vast landscape of Medical Artificial Intelligence. The book is suitable for reading by computer science researchers, medical professionals, healthcare informatics, and medical imaging researchers interested in understanding the potential of artificial intelligence in healthcare. It serves as a compass for navigating the artificial intelligence-driven healthcare landscape.

Synthetic Data for Deep Learning

Author : Sergey I. Nikolenko
Publisher : Springer Nature
ISBN 13 : 3030751783
Total Pages : 348 pages
Book Rating : 4.0/5 (37 download)

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Book Synopsis Synthetic Data for Deep Learning by : Sergey I. Nikolenko

Download or read book Synthetic Data for Deep Learning written by Sergey I. Nikolenko and published by Springer Nature. This book was released on 2021-06-26 with total page 348 pages. Available in PDF, EPUB and Kindle. Book excerpt: This is the first book on synthetic data for deep learning, and its breadth of coverage may render this book as the default reference on synthetic data for years to come. The book can also serve as an introduction to several other important subfields of machine learning that are seldom touched upon in other books. Machine learning as a discipline would not be possible without the inner workings of optimization at hand. The book includes the necessary sinews of optimization though the crux of the discussion centers on the increasingly popular tool for training deep learning models, namely synthetic data. It is expected that the field of synthetic data will undergo exponential growth in the near future. This book serves as a comprehensive survey of the field. In the simplest case, synthetic data refers to computer-generated graphics used to train computer vision models. There are many more facets of synthetic data to consider. In the section on basic computer vision, the book discusses fundamental computer vision problems, both low-level (e.g., optical flow estimation) and high-level (e.g., object detection and semantic segmentation), synthetic environments and datasets for outdoor and urban scenes (autonomous driving), indoor scenes (indoor navigation), aerial navigation, and simulation environments for robotics. Additionally, it touches upon applications of synthetic data outside computer vision (in neural programming, bioinformatics, NLP, and more). It also surveys the work on improving synthetic data development and alternative ways to produce it such as GANs. The book introduces and reviews several different approaches to synthetic data in various domains of machine learning, most notably the following fields: domain adaptation for making synthetic data more realistic and/or adapting the models to be trained on synthetic data and differential privacy for generating synthetic data with privacy guarantees. This discussion is accompanied by an introduction into generative adversarial networks (GAN) and an introduction to differential privacy.