Synthetic Data And Generative Ai
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Synthetic Data And Generative Ai
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Author : Anand Vemula
language : en
Publisher: Independently Published
Release Date : 2024-06-04
Synthetic Data And Generative Ai written by Anand Vemula and has been published by Independently Published this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-06-04 with Computers categories.
In the ever-evolving world of Artificial Intelligence (AI), data is king. But real-world data often comes with limitations: scarcity, privacy concerns, and inherent biases. This is where synthetic data steps in. Synthetic Data and Generative AI: A Developer's Handbook empowers you to harness the power of synthetic data creation using generative AI models. This comprehensive guide equips you with the knowledge and tools to develop and leverage synthetic data for your AI projects. Part 1: Introduction Grasp the challenges of real-world data and discover how synthetic data addresses them. Understand the fundamental concepts of generative AI and its role in creating realistic synthetic data. Part 2: Unveiling the Power of Synthetic Data Explore the numerous benefits of synthetic data, including overcoming data scarcity, mitigating bias, and ensuring data privacy. Witness the vast potential of synthetic data across various industries, from self-driving cars and healthcare to finance and risk management. Part 3: Generative AI Techniques Demystified Dive deep into the two pillars of generative AI: Generative Adversarial Networks (GANs) and Variational Autoencoders (VAEs). Learn how these models work, their strengths and weaknesses, and how to choose the right technique for your specific needs. Part 4: Building and Training Generative Models for Developers Gain practical knowledge on pre-processing data and selecting appropriate generative models for your project. Follow step-by-step tutorials (with code examples linked to online resources) to train your own generative models and generate synthetic data tailored to your requirements. Part 5: The Future Landscape Explore cutting-edge advancements in Explainable AI (XAI) for synthetic data generation, ensuring transparency and trust in your models. Learn how to integrate synthetic data generation into your machine learning pipelines for a seamless and efficient AI development workflow. Part 6: Responsible Development and Conclusion Uncover the ethical considerations surrounding synthetic data, including potential biases and the importance of fairness. Gain insights into best practices for developing trustworthy and responsible AI systems using synthetic data. Synthetic Data and Generative AI: A Developer's Handbook is your one-stop guide to mastering this transformative technology. With its clear explanations, practical tutorials, and exploration of future trends, this book empowers you to unlock the full potential of AI in your projects.
Synthetic Data And Generative Ai
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Author : Vincent Granville
language : en
Publisher: Elsevier
Release Date : 2024-01-09
Synthetic Data And Generative Ai written by Vincent Granville and has been published by Elsevier this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-01-09 with Computers categories.
Synthetic Data and Generative AI covers the foundations of machine learning, with modern approaches to solving complex problems and the systematic generation and use of synthetic data. Emphasis is on scalability, automation, testing, optimizing, and interpretability (explainable AI). For instance, regression techniques – including logistic and Lasso – are presented as a single method, without using advanced linear algebra. Confidence regions and prediction intervals are built using parametric bootstrap, without statistical models or probability distributions. Models (including generative models and mixtures) are mostly used to create rich synthetic data to test and benchmark various methods. - Emphasizes numerical stability and performance of algorithms (computational complexity) - Focuses on explainable AI/interpretable machine learning, with heavy use of synthetic data and generative models, a new trend in the field - Includes new, easier construction of confidence regions, without statistics, a simple alternative to the powerful, well-known XGBoost technique - Covers automation of data cleaning, favoring easier solutions when possible - Includes chapters dedicated fully to synthetic data applications: fractal-like terrain generation with the diamond-square algorithm, and synthetic star clusters evolving over time and bound by gravity
Practical Simulations For Machine Learning
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Author : Paris Buttfield-Addison
language : en
Publisher: "O'Reilly Media, Inc."
Release Date : 2022-06-07
Practical Simulations For Machine Learning written by Paris Buttfield-Addison and has been published by "O'Reilly Media, Inc." this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-06-07 with Computers categories.
Simulation and synthesis are core parts of the future of AI and machine learning. Consider: programmers, data scientists, and machine learning engineers can create the brain of a self-driving car without the car. Rather than use information from the real world, you can synthesize artificial data using simulations to train traditional machine learning models.That's just the beginning. With this practical book, you'll explore the possibilities of simulation- and synthesis-based machine learning and AI, concentrating on deep reinforcement learning and imitation learning techniques. AI and ML are increasingly data driven, and simulations are a powerful, engaging way to unlock their full potential. You'll learn how to: Design an approach for solving ML and AI problems using simulations with the Unity engine Use a game engine to synthesize images for use as training data Create simulation environments designed for training deep reinforcement learning and imitation learning models Use and apply efficient general-purpose algorithms for simulation-based ML, such as proximal policy optimization Train a variety of ML models using different approaches Enable ML tools to work with industry-standard game development tools, using PyTorch, and the Unity ML-Agents and Perception Toolkits
Generative Ai
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Author : Chad Hendren
language : en
Publisher: Independently Published
Release Date : 2024-07-03
Generative Ai written by Chad Hendren and has been published by Independently Published this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-07-03 with Business & Economics categories.
In the rapidly evolving landscape of customer experience (CX), businesses are constantly seeking innovative ways to understand, predict, and enhance customer interactions. This groundbreaking book, "Synthetic Data Revolution in Customer Experience: Powering the Future with Generative AI," offers a comprehensive guide to harnessing the power of synthetic data and generative AI in CX applications. From proving CX hypotheses and conducting risk-free experiments to training robust language models, this book demonstrates why synthetic data is not just an option but a necessity in today's data-driven world. It explores how Canary and telemetry testing with synthetic data can provide invaluable insights without compromising real customer data, and delves into the critical role of synthetic data in creating and refining Large Language Models. Written for CX professionals, data scientists, and business leaders alike, this book provides practical strategies for leveraging generative AI to create large, powerful datasets. It offers step-by-step guidance on applying these datasets in customer experience application development and ongoing tests in production environments. Readers will learn: Why synthetic data is crucial for proving CX application hypotheses How to use Canary and telemetry testing with synthetic data for risk-free experimentation The importance of synthetic data in training Large Language Models Practical applications of generative AI in creating robust CX datasets Strategies for implementing synthetic data in CX application development and testing This book is an essential resource for anyone looking to stay ahead in the competitive landscape of customer experience. By embracing the synthetic data revolution, businesses can unlock new levels of innovation, efficiency, and customer satisfaction.
Practical Synthetic Data Generation
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Author : Khaled El Emam
language : en
Publisher: "O'Reilly Media, Inc."
Release Date : 2020-05-19
Practical Synthetic Data Generation written by Khaled El Emam and has been published by "O'Reilly Media, Inc." this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-05-19 with Computers categories.
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
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.
Generative Ai
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Author : Ravindra Das
language : en
Publisher: CRC Press
Release Date : 2024-10-10
Generative Ai written by Ravindra Das and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-10-10 with Computers categories.
The cybersecurity landscape is changing, for sure. For example, one of the oldest threat variants is that of phishing. It evolved in the early 1990s, but even today it is still being used as a primary threat variant and has now become much more sophisticated, covert, and stealthy in nature. For example, it can be used to launch ransomware, social engineering, and extortion attacks. The advent of Generative AI is making this much worse. For example, a cyberattacker can now use something like ChatGPT to craft the content for phishing emails that are so convincing that it is almost impossible to tell the difference between what is real and what is fake. This is also clearly evident in the use of deepfakes, where fake images of real people are replicated to create videos to lure unsuspecting victims to a fake website. But Generative AI can also be used for the good to combat Phishing Attacks. This is the topic of this book. In this, we cover the following: A review of phishing A review of AI, Neural Networks, and Machine Learning A review of Natural Language Processing, Generative AI, and the Digital Person A proposed solution as to how Generative AI can combat phishing attacks as they relate to Privileged Access accounts
Flow Architectures
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Author : James Urquhart
language : en
Publisher: O'Reilly Media
Release Date : 2021-01-06
Flow Architectures written by James Urquhart and has been published by O'Reilly Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-01-06 with Computers categories.
Software development today is embracing events and streaming data, which optimizes not only how technology interacts but also how businesses integrate with one another to meet customer needs. This phenomenon, called flow, consists of patterns and standards that determine which activity and related data is communicated between parties over the internet. This book explores critical implications of that evolution: What happens when events and data streams help you discover new activity sources to enhance existing businesses or drive new markets? What technologies and architectural patterns can position your company for opportunities enabled by flow? James Urquhart, global field CTO at VMware, guides enterprise architects, software developers, and product managers through the process. Learn the benefits of flow dynamics when businesses, governments, and other institutions integrate via events and data streams Understand the value chain for flow integration through Wardley mapping visualization and promise theory modeling Walk through basic concepts behind today's event-driven systems marketplace Learn how today's integration patterns will influence the real-time events flow in the future Explore why companies should architect and build software today to take advantage of flow in coming years
Integration Strategies Of Generative Ai In Higher Education
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Author : Arinushkina, Anna A.
language : en
Publisher: IGI Global
Release Date : 2024-09-27
Integration Strategies Of Generative Ai In Higher Education written by Arinushkina, Anna A. and has been published by IGI Global this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-09-27 with Education categories.
Amidst the rapid evolution of educational technology, a pressing challenge confronts higher education institutions: how to effectively integrate generative artificial intelligence (AI) into their existing frameworks. As universities strive to adapt to the digital age, they are met with the complexities of incorporating AI-driven solutions to enhance teaching, learning, and administrative processes. However, the lack of comprehensive strategies and guidance hinders their ability to leverage AI's full potential, leaving educators and administrators grappling with uncertainty. In response to this critical dilemma, Integration Strategies of Generative AI in Higher Education emerges as a guide for clarity and innovation. By offering methodological insights and practical frameworks, this book equips higher education stakeholders with the tools needed to navigate the intricacies of AI integration. From curriculum enhancement to AI-driven content creation, the book provides actionable strategies tailored to the unique needs and challenges of higher education institutions.
Practical Guide To Machine Learning Nlp And Generative Ai Libraries Algorithms And Applications
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Author : T. Mariprasath
language : en
Publisher: CRC Press
Release Date : 2024-12-23
Practical Guide To Machine Learning Nlp And Generative Ai Libraries Algorithms And Applications written by T. Mariprasath and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-12-23 with Computers categories.
This is an essential resource for beginners and experienced practitioners in machine learning. This comprehensive guide covers a broad spectrum of machine learning topics, starting with an in-depth exploration of popular machine learning libraries. Readers will gain a thorough understanding of Scikit-learn, TensorFlow, PyTorch, Keras, and other pivotal libraries like XGBoost, LightGBM, and CatBoost, which are integral for efficient model development and deployment. The book delves into various neural network architectures, providing readers with a solid foundation in understanding and applying these models. Beginning with the basics of the Perceptron and its application in digit classification, it progresses to more complex structures such as multilayer perceptrons for financial forecasting, radial basis function networks for air quality prediction, and convolutional neural networks (CNNs) for image classification. Additionally, the book covers recurrent neural networks (RNNs) and their variants like long short-term memory (LSTM) and gated recurrent units (GRUs), which are crucial for time-series analysis and sequential data applications. Supervised machine learning algorithms are meticulously explained, with practical examples to illustrate their application. The book covers logistic regression and its use in predicting sports outcomes, decision trees for plant classification, random forests for traffic prediction, and support vector machines for house price prediction. Gradient boosting machines and their applications in genomics, AdaBoost for bioinformatics data classification, and extreme gradient boosting (XGBoost) for churn prediction are also discussed, providing readers with a robust toolkit for various predictive tasks. Unsupervised learning algorithms are another significant focus of the book, introducing readers to techniques for uncovering hidden patterns in data. Hierarchical clustering for gene expression data analysis, principal component analysis (PCA) for climate predictions, and singular value decomposition (SVD) for signal denoising are thoroughly explained. The book also explores applications like robot navigation and network security, demonstrating the versatility of these techniques. Natural language processing (NLP) is comprehensively covered, highlighting its fundamental concepts and various applications. The book discusses the overview of NLP, its fundamental concepts, and its diverse applications such as chatbots, virtual assistants, clinical NLP applications, and social media analytics. Detailed sections on text pre-processing, syntactic analysis, machine translation, text classification, named entity recognition, and sentiment analysis equip readers with the knowledge to build sophisticated NLP models. The final chapters of the book explore generative AI, including generative adversarial networks (GANs) for image generation, variational autoencoders for vibrational encoder training, and autoregressive models for time series forecasting. It also delves into Markov chain models for text generation, Boltzmann machines for pattern recognition, and deep belief networks for financial forecasting. Special attention is given to the application of recurrent neural networks (RNNs) for generation tasks, such as wind power plant predictions and battery range prediction, showcasing the practical implementations of generative AI in various fields.