synthetic-data-for-deep-learning

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Synthetic Data For Deep Learning

Author : Sergey I. Nikolenko
ISBN : 9783030751784
Genre : Computers
File Size : 35. 45 MB
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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.

Synthetic Data For Deep Learning

Author : Necmi Gürsakal
ISBN : 1484285867
Genre : Computers
File Size : 36. 35 MB
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Data is the indispensable fuel that drives the decision making of everything from governments, to major corporations, to sports teams. Its value is almost beyond measure. But what if that data is either unavailable or problematic to access? That’s where synthetic data comes in. This book will show you how to generate synthetic data and use it to maximum effect. Synthetic Data for Deep Learning begins by tracing the need for and development of synthetic data before delving into the role it plays in machine learning and computer vision. You’ll gain insight into how synthetic data can be used to study the benefits of autonomous driving systems and to make accurate predictions about real-world data. You’ll work through practical examples of synthetic data generation using Python and R, placing its purpose and methods in a real-world context. Generative Adversarial Networks (GANs) are also covered in detail, explaining how they work and their potential applications. After completing this book, you’ll have the knowledge necessary to generate and use synthetic data to enhance your corporate, scientific, or governmental decision making. What You Will Learn Create synthetic data for tabular data with Python Understand how artificial neural networks can be used to create synthetic data Master the benefits and challenges of synthetic data Use the GPT-3 algorithm to improve the quality of synthetic data Who This Book Is For Those who want to learn about synthetic data and its applications, especially professionals working in the field of machine learning and computer vision. This book will also be useful for graduate and doctoral students interested in this subject.

Practical Synthetic Data Generation

Author : Khaled El Emam
ISBN : 9781492072690
Genre : Computers
File Size : 68. 19 MB
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Building and testing machine learning models requires access to large and diverse data. But where can you find usable datasets without running into privacy issues? This practical book introduces techniques for generating synthetic data—fake data generated from real data—so you can perform secondary analysis to do research, understand customer behaviors, develop new products, or generate new revenue. Data scientists will learn how synthetic data generation provides a way to make such data broadly available for secondary purposes while addressing many privacy concerns. Analysts will learn the principles and steps for generating synthetic data from real datasets. And business leaders will see how synthetic data can help accelerate time to a product or solution. This book describes: Steps for generating synthetic data using multivariate normal distributions Methods for distribution fitting covering different goodness-of-fit metrics How to replicate the simple structure of original data An approach for modeling data structure to consider complex relationships Multiple approaches and metrics you can use to assess data utility How analysis performed on real data can be replicated with synthetic data Privacy implications of synthetic data and methods to assess identity disclosure

Machine Learning With Amazon Sagemaker Cookbook

Author : Joshua Arvin Lat
ISBN : 9781800566125
Genre : Computers
File Size : 21. 90 MB
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A step-by-step solution-based guide to preparing building, training, and deploying high-quality machine learning models with Amazon SageMaker Key FeaturesPerform ML experiments with built-in and custom algorithms in SageMakerExplore proven solutions when working with TensorFlow, PyTorch, Hugging Face Transformers, and scikit-learnUse the different features and capabilities of SageMaker to automate relevant ML processesBook Description Amazon SageMaker is a fully managed machine learning (ML) service that helps data scientists and ML practitioners manage ML experiments. In this book, you'll use the different capabilities and features of Amazon SageMaker to solve relevant data science and ML problems. This step-by-step guide features 80 proven recipes designed to give you the hands-on machine learning experience needed to contribute to real-world experiments and projects. You'll cover the algorithms and techniques that are commonly used when training and deploying NLP, time series forecasting, and computer vision models to solve ML problems. You'll explore various solutions for working with deep learning libraries and frameworks such as TensorFlow, PyTorch, and Hugging Face Transformers in Amazon SageMaker. You'll also learn how to use SageMaker Clarify, SageMaker Model Monitor, SageMaker Debugger, and SageMaker Experiments to debug, manage, and monitor multiple ML experiments and deployments. Moreover, you'll have a better understanding of how SageMaker Feature Store, Autopilot, and Pipelines can meet the specific needs of data science teams. By the end of this book, you'll be able to combine the different solutions you've learned as building blocks to solve real-world ML problems. What you will learnTrain and deploy NLP, time series forecasting, and computer vision models to solve different business problemsPush the limits of customization in SageMaker using custom container imagesUse AutoML capabilities with SageMaker Autopilot to create high-quality modelsWork with effective data analysis and preparation techniquesExplore solutions for debugging and managing ML experiments and deploymentsDeal with bias detection and ML explainability requirements using SageMaker ClarifyAutomate intermediate and complex deployments and workflows using a variety of solutionsWho this book is for This book is for developers, data scientists, and machine learning practitioners interested in using Amazon SageMaker to build, analyze, and deploy machine learning models with 80 step-by-step recipes. All you need is an AWS account to get things running. Prior knowledge of AWS, machine learning, and the Python programming language will help you to grasp the concepts covered in this book more effectively.

Advances In Neural Computation Machine Learning And Cognitive Research Iv

Author : Boris Kryzhanovsky
ISBN : 9783030605773
Genre : Technology & Engineering
File Size : 65. 2 MB
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This book describes new theories and applications of artificial neural networks, with a special focus on answering questions in neuroscience, biology and biophysics and cognitive research. It covers a wide range of methods and technologies, including deep neural networks, large scale neural models, brain computer interface, signal processing methods, as well as models of perception, studies on emotion recognition, self-organization and many more. The book includes both selected and invited papers presented at the XXII International Conference on Neuroinformatics, held on October 12-16, 2020, Moscow, Russia.

Machine Learning And Knowledge Discovery In Databases

Author : Michele Berlingerio
ISBN : 9783030109257
Genre : Computers
File Size : 54. 85 MB
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The three volume proceedings LNAI 11051 – 11053 constitutes the refereed proceedings of the European Conference on Machine Learning and Knowledge Discovery in Databases, ECML PKDD 2018, held in Dublin, Ireland, in September 2018. The total of 131 regular papers presented in part I and part II was carefully reviewed and selected from 535 submissions; there are 52 papers in the applied data science, nectar and demo track. The contributions were organized in topical sections named as follows: Part I: adversarial learning; anomaly and outlier detection; applications; classification; clustering and unsupervised learning; deep learningensemble methods; and evaluation. Part II: graphs; kernel methods; learning paradigms; matrix and tensor analysis; online and active learning; pattern and sequence mining; probabilistic models and statistical methods; recommender systems; and transfer learning. Part III: ADS data science applications; ADS e-commerce; ADS engineering and design; ADS financial and security; ADS health; ADS sensing and positioning; nectar track; and demo track.

Artificial Intelligence And Deep Learning In Pathology

Author : Stanley Cohen
ISBN : 9780323675376
Genre : Medical
File Size : 43. 81 MB
Format : PDF
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Recent advances in computational algorithms, along with the advent of whole slide imaging as a platform for embedding artificial intelligence (AI), are transforming pattern recognition and image interpretation for diagnosis and prognosis. Yet most pathologists have just a passing knowledge of data mining, machine learning, and AI, and little exposure to the vast potential of these powerful new tools for medicine in general and pathology in particular. In Artificial Intelligence and Deep Learning in Pathology, Dr. Stanley Cohen covers the nuts and bolts of all aspects of machine learning, up to and including AI, bringing familiarity and understanding to pathologists at all levels of experience. Focuses heavily on applications in medicine, especially pathology, making unfamiliar material accessible and avoiding complex mathematics whenever possible. Covers digital pathology as a platform for primary diagnosis and augmentation via deep learning, whole slide imaging for 2D and 3D analysis, and general principles of image analysis and deep learning. Discusses and explains recent accomplishments such as algorithms used to diagnose skin cancer from photographs, AI-based platforms developed to identify lesions of the retina, using computer vision to interpret electrocardiograms, identifying mitoses in cancer using learning algorithms vs. signal processing algorithms, and many more.

Privacy In Statistical Databases

Author : Josep Domingo-Ferrer
ISBN : 9783031139451
Genre : Computers
File Size : 68. 1 MB
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​This book constitutes the refereed proceedings of the International Conference on Privacy in Statistical Databases, PSD 2022, held in Paris, France, during September 21-23, 2022. The 25 papers presented in this volume were carefully reviewed and selected from 45 submissions. They were organized in topical sections as follows: Privacy models; tabular data; disclosure risk assessment and record linkage; privacy-preserving protocols; unstructured and mobility data; synthetic data; machine learning and privacy; and case studies.

Soft Computing And Signal Processing

Author : V. Sivakumar Reddy
ISBN : 9789811670886
Genre : Electronic books
File Size : 54. 98 MB
Format : PDF
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This book presents selected research papers on current developments in the fields of soft computing and signal processing from the Fourth International Conference on Soft Computing and Signal Processing (ICSCSP 2021). The book covers topics such as soft sets, rough sets, fuzzy logic, neural networks, genetic algorithms and machine learning and discusses various aspects of these topics, e.g., technological considerations, product implementation and application issues.

Heterogeneous Data Management Polystores And Analytics For Healthcare

Author : El Kindi Rezig
ISBN : 9783030936631
Genre :
File Size : 90. 98 MB
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Security And Artificial Intelligence

Author : Lejla Batina
ISBN : 9783030987954
Genre :
File Size : 54. 44 MB
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Accelerating Ai With Synthetic Data

Author : Khaled Emam
ISBN : OCLC:1192538236
Genre :
File Size : 71. 69 MB
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Recently, data scientists have found effective methods to generate high-quality synthetic data. That's good news for companies seeking large amounts of data to train and build artificial intelligence and machine learning models. This report provides an overview of synthetic data generation that not only focuses on business value and use cases but also provides some practical techniques for using synthetic data. Author Khaled El Emam, cofounder and Director of Replica Analytics and Professor at the University of Ottawa, helps data analytics leadership understand the options so they can get started building their own training sets. With the help of several industry use cases, you'll learn how synthetic data can accelerate machine learning projects in your company. As advances in synthetic data generation continue, broad adoption of this approach will quickly follow. Learn what synthetic data is and how it can accelerate machine learning model development Understand how synthetic data is generated-and why these datasets are similar to real data Explore the process and best practices for generating synthetic datasets Examine case studies of synthetic data use in industries including manufacturing, healthcare, financial services, and transportation Learn key requirements for future work and improvements to synthetic data.

Deep Learning Applications

Author : M. Arif Wani
ISBN : 9789811518164
Genre : Technology & Engineering
File Size : 30. 13 MB
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This book presents a compilation of selected papers from the 17th IEEE International Conference on Machine Learning and Applications (IEEE ICMLA 2018), focusing on use of deep learning technology in application like game playing, medical applications, video analytics, regression/classification, object detection/recognition and robotic control in industrial environments. It highlights novel ways of using deep neural networks to solve real-world problems, and also offers insights into deep learning architectures and algorithms, making it an essential reference guide for academic researchers, professionals, software engineers in industry, and innovative product developers.

Service Oriented Holonic And Multi Agent Manufacturing Systems For Industry Of The Future

Author : Damien Trentesaux
ISBN : 9783030809065
Genre : Technology & Engineering
File Size : 79. 75 MB
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This book approaches its subject matter by promoting concepts, methods and solutions for the digital transformation of manufacturing through service orientation in holonic and agent-based control with distributed intelligence. The scientific theme of the book concerns “Manufacturing as a Service”, developed by virtualizing and encapsulating manufacturing resources, activities and controls into cloud networked services in an open perspective that spans models from shop floor resource allocation to enterprise infrastructure sharing. The papers included in the application space have a profound human dedication and aim at solving societal needs serving the partnership of the future—people and industry in the era of Society 5.0. The book’s readership includes researchers and engineers working in manufacturing, supply chains and logistics areas who innovate, develop and use digital control solutions and students enrolled in Engineering and Service Science programs.

Hands On Unsupervised Learning Using Python

Author : Ankur A. Patel
ISBN : 9781492035596
Genre :
File Size : 50. 9 MB
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Many industry experts consider unsupervised learning the next frontier in artificial intelligence, one that may hold the key to general artificial intelligence. Since the majority of the world's data is unlabeled, conventional supervised learning cannot be applied. Unsupervised learning, on the other hand, can be applied to unlabeled datasets to discover meaningful patterns buried deep in the data, patterns that may be near impossible for humans to uncover. Author Ankur Patel shows you how to apply unsupervised learning using two simple, production-ready Python frameworks: Scikit-learn and TensorFlow using Keras. With code and hands-on examples, data scientists will identify difficult-to-find patterns in data and gain deeper business insight, detect anomalies, perform automatic feature engineering and selection, and generate synthetic datasets. All you need is programming and some machine learning experience to get started. Compare the strengths and weaknesses of the different machine learning approaches: supervised, unsupervised, and reinforcement learning Set up and manage machine learning projects end-to-end Build an anomaly detection system to catch credit card fraud Clusters users into distinct and homogeneous groups Perform semisupervised learning Develop movie recommender systems using restricted Boltzmann machines Generate synthetic images using generative adversarial networks

Deep Learning With Tensorflow 2 And Keras

Author : Antonio Gulli
ISBN : 9781838827724
Genre : Computers
File Size : 26. 53 MB
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Build machine and deep learning systems with the newly released TensorFlow 2 and Keras for the lab, production, and mobile devices Key FeaturesIntroduces and then uses TensorFlow 2 and Keras right from the startTeaches key machine and deep learning techniquesUnderstand the fundamentals of deep learning and machine learning through clear explanations and extensive code samplesBook Description Deep Learning with TensorFlow 2 and Keras, Second Edition teaches neural networks and deep learning techniques alongside 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 is the machine learning library of choice for professional applications, while Keras offers a simple and powerful Python API for accessing TensorFlow. TensorFlow 2 provides full Keras integration, making advanced machine learning easier and more convenient than ever before. This book also introduces neural networks with TensorFlow, runs through the main applications (regression, ConvNets (CNNs), GANs, RNNs, NLP), covers two working example apps, and then dives into TF in production, TF mobile, and using TensorFlow with AutoML. What you will learnBuild machine learning and deep learning systems with TensorFlow 2 and the Keras APIUse Regression analysis, the most popular approach to machine learningUnderstand ConvNets (convolutional neural networks) and how they are essential for deep learning systems such as image classifiersUse GANs (generative adversarial networks) to create new data that fits with existing patternsDiscover RNNs (recurrent neural networks) that can process sequences of input intelligently, using one part of a sequence to correctly interpret anotherApply deep learning to natural human language and interpret natural language texts to produce an appropriate responseTrain your models on the cloud and put TF to work in real environmentsExplore how Google tools can automate simple ML workflows without the need for complex modelingWho this book is for This 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 2, and AutoML to build machine learning systems. Some knowledge of machine learning is expected.

Proceedings Of The International Conference On Artificial Intelligence And Computer Vision Aicv2021

Author : Aboul Ella Hassanien
ISBN : 9783030763466
Genre : Technology & Engineering
File Size : 37. 62 MB
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This book presents the 2nd International Conference on Artificial Intelligence and Computer Visions (AICV 2021) proceeding, which took place in Settat, Morocco, from June 28- to 30, 2021. AICV 2021 is organized by the Scientific Research Group in Egypt (SRGE) and the Computer, Networks, Mobility and Modeling Laboratory (IR2M), Hassan 1st University, Faculty of Sciences Techniques, Settat, Morocco. This international conference highlighted essential research and developments in the fields of artificial intelligence and computer visions. The book is divided into sections, covering the following topics: Deep Learning and Applications; Smart Grid, Internet of Things, and Mobil Applications; Machine Learning and Metaheuristics Optimization; Business Intelligence and Applications; Machine Vision, Robotics, and Speech Recognition; Advanced Machine Learning Technologies; Big Data, Digital Transformation, AI and Network Analysis; Cybersecurity; Feature Selection, Classification, and Applications.

Business Information Systems Workshops

Author : Witold Abramowicz
ISBN : 9783030611460
Genre : Computers
File Size : 35. 64 MB
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This book constitutes revised papers from the five workshops which were held during June 2020 at the 23rd International Conference on Business Information Systems, BIS 2020. The conference was planned to take place in Colorado Springs, CO, USA. Due to the COVID-19 pandemic it changed to a virtual format. There was a total of 54 submissions to all workshops of which 26 papers were accepted for publication. The workshops included in this volume are: BITA 2020: 11th Workshop on Business and IT Alignment BSCT 2020: 3rd Workshop on Blockchain and Smart Contract Technologies DigEX 2020: 2nd International Workshop on transforming the Digital Customer Experience iCRM 2020: 5th International Workshop on Intelligent Data Analysis in Integrated Social CRM QOD 2020: 3rd Workshop on Quality of Open Data

Database And Expert Systems Applications Dexa 2022 Workshops

Author : Gabriele Kotsis
ISBN : 9783031143434
Genre : Computers
File Size : 46. 30 MB
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This volume constitutes the refereed proceedings of the workshops held at the 33rd International Conference on Database and Expert Systems Applications, DEXA 2022, held in Vienna, Austria, in August 2022: The 6th International Workshop on Cyber-Security and Functional Safety in Cyber-Physical Systems (IWCFS 2022); 4th International Workshop on Machine Learning and Knowledge Graphs (MLKgraphs 2022); 2nd International Workshop on Time Ordered Data (ProTime2022); 2nd International Workshop on AI System Engineering: Math, Modelling and Software (AISys2022); 1st International Workshop on Distributed Ledgers and Related Technologies (DLRT2022); 1st International Workshop on Applied Research, Technology Transfer and Knowledge Exchange in Software and Data Science (ARTE2022). The 40 papers were thoroughly reviewed and selected from 62 submissions, and discuss a range of topics including: knowledge discovery, biological data, cyber security, cyber-physical system, machine learning, knowledge graphs, information retriever, data base, and artificial intelligence.

Neural Information Processing

Author : Derong Liu
ISBN : 9783319700960
Genre : Computers
File Size : 71. 57 MB
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The six volume set LNCS 10634, LNCS 10635, LNCS 10636, LNCS 10637, LNCS 10638, and LNCS 10639 constituts the proceedings of the 24rd International Conference on Neural Information Processing, ICONIP 2017, held in Guangzhou, China, in November 2017. The 563 full papers presented were carefully reviewed and selected from 856 submissions. The 6 volumes are organized in topical sections on Machine Learning, Reinforcement Learning, Big Data Analysis, Deep Learning, Brain-Computer Interface, Computational Finance, Computer Vision, Neurodynamics, Sensory Perception and Decision Making, Computational Intelligence, Neural Data Analysis, Biomedical Engineering, Emotion and Bayesian Networks, Data Mining, Time-Series Analysis, Social Networks, Bioinformatics, Information Security and Social Cognition, Robotics and Control, Pattern Recognition, Neuromorphic Hardware and Speech Processing.

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