Data Augmentation With Python
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Data Augmentation With Python
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Author : Duc Haba
language : en
Publisher: Packt Publishing Ltd
Release Date : 2023-04-28
Data Augmentation With Python written by Duc Haba and has been published by Packt Publishing Ltd this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-04-28 with Computers categories.
Boost your AI and generative AI accuracy using real-world datasets with over 150 functional object-oriented methods and open source libraries Purchase of the print or Kindle book includes a free PDF eBook Key Features Explore beautiful, customized charts and infographics in full color Work with fully functional OO code using open source libraries in the Python Notebook for each chapter Unleash the potential of real-world datasets with practical data augmentation techniques Book Description Data is paramount in AI projects, especially for deep learning and generative AI, as forecasting accuracy relies on input datasets being robust. Acquiring additional data through traditional methods can be challenging, expensive, and impractical, and data augmentation offers an economical option to extend the dataset. The book teaches you over 20 geometric, photometric, and random erasing augmentation methods using seven real-world datasets for image classification and segmentation. You'll also review eight image augmentation open source libraries, write object-oriented programming (OOP) wrapper functions in Python Notebooks, view color image augmentation effects, analyze safe levels and biases, as well as explore fun facts and take on fun challenges. As you advance, you'll discover over 20 character and word techniques for text augmentation using two real-world datasets and excerpts from four classic books. The chapter on advanced text augmentation uses machine learning to extend the text dataset, such as Transformer, Word2vec, BERT, GPT-2, and others. While chapters on audio and tabular data have real-world data, open source libraries, amazing custom plots, and Python Notebook, along with fun facts and challenges. By the end of this book, you will be proficient in image, text, audio, and tabular data augmentation techniques. What you will learn Write OOP Python code for image, text, audio, and tabular data Access over 150,000 real-world datasets from the Kaggle website Analyze biases and safe parameters for each augmentation method Visualize data using standard and exotic plots in color Discover 32 advanced open source augmentation libraries Explore machine learning models, such as BERT and Transformer Meet Pluto, an imaginary digital coding companion Extend your learning with fun facts and fun challenges Who this book is for This book is for data scientists and students interested in the AI discipline. Advanced AI or deep learning skills are not required; however, knowledge of Python programming and familiarity with Jupyter Notebooks are essential to understanding the topics covered in this book.
Data Augmentation With Python
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Author : Duc Haba
language : en
Publisher:
Release Date : 2023-04-28
Data Augmentation With Python written by Duc Haba and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-04-28 with categories.
Boost your AI and generative AI accuracy using real-world datasets with over 150 functional object-oriented methods and open source libraries Purchase of the print or Kindle book includes a free PDF eBook Key Features: Explore beautiful, customized charts and infographics in full color Work with fully functional OO code using open source libraries in the Python Notebook for each chapter Unleash the potential of real-world datasets with practical data augmentation techniques Book Description: Data is paramount in AI projects, especially for deep learning and generative AI, as forecasting accuracy relies on input datasets being robust. Acquiring additional data through traditional methods can be challenging, expensive, and impractical, and data augmentation offers an economical option to extend the dataset. The book teaches you over 20 geometric, photometric, and random erasing augmentation methods using seven real-world datasets for image classification and segmentation. You'll also review eight image augmentation open source libraries, write object-oriented programming (OOP) wrapper functions in Python Notebooks, view color image augmentation effects, analyze safe levels and biases, as well as explore fun facts and take on fun challenges. As you advance, you'll discover over 20 character and word techniques for text augmentation using two real-world datasets and excerpts from four classic books. The chapter on advanced text augmentation uses machine learning to extend the text dataset, such as Transformer, Word2vec, BERT, GPT-2, and others. While chapters on audio and tabular data have real-world data, open source libraries, amazing custom plots, and Python Notebook, along with fun facts and challenges. By the end of this book, you will be proficient in image, text, audio, and tabular data augmentation techniques. What You Will Learn: Write OOP Python code for image, text, audio, and tabular data Access over 150,000 real-world datasets from the Kaggle website Analyze biases and safe parameters for each augmentation method Visualize data using standard and exotic plots in color Discover 32 advanced open source augmentation libraries Explore machine learning models, such as BERT and Transformer Meet Pluto, an imaginary digital coding companion Extend your learning with fun facts and fun challenges Who this book is for: This book is for data scientists and students interested in the AI discipline. Advanced AI or deep learning skills are not required; however, knowledge of Python programming and familiarity with Jupyter Notebooks are essential to understanding the topics covered in this book.
Generative Ai Essentials
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Author : Dr. Priyanka Singh
language : en
Publisher: BPB Publications
Release Date : 2025-01-07
Generative Ai Essentials written by Dr. Priyanka Singh and has been published by BPB Publications this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-01-07 with Computers categories.
DESCRIPTION Generative AI is changing the way we think about creativity and problem-solving. This book is your go-to guide for understanding and working with this exciting technology. This book offers a clear introduction to generative AI, starting with basics like machine learning and deep learning. It explains key models, including GANs and VAEs, breaking down their architectures and training methods. You will discover how Transformer models like GPT have transformed natural language processing and enabled advancements in language generation. The book explores practical applications such as image synthesis, style transfer, and text generation, showing how generative AI merges technology with creativity. Advanced topics like reinforcement learning, AI ethics, and bias are also covered. Practical tips for creating your own generative AI models, along with insights into the future of this groundbreaking field, making it an essential resource for AI enthusiasts and professionals. By the end of this book, you will have a firm grasp of generative AI concepts and practical skills to get you started. You will be well-prepared to use cloud platforms like AWS, Azure, and GCP to build and launch powerful generative AI projects. From creating realistic images to crafting natural text, you will explore hands-on examples while tackling important ethical questions. This book gives you the skills and confidence to explore the limitless potential of generative AI. KEY FEATURES ● Learn GANs, VAEs, and Transformers with real-world applications. ● Build scalable generative AI models using AWS, Azure, and GCP. ● Explore ethical AI, creative projects, and future trends in technology. WHAT YOU WILL LEARN ● Build foundational knowledge of generative AI principles and models. ● Apply machine learning and deep learning for creative content generation. ● Leverage GANs, VAEs, and Transformer models in real-world scenarios. ● Master cloud tools for scalable generative AI development. ● Address ethical challenges and implement responsible AI practices. ● Explore advanced applications and future directions of generative AI WHO THIS BOOK IS FOR This book is designed for data scientists, machine learning engineers, software developers, cloud professionals, educators, students, and creative professionals. TABLE OF CONTENTS 1. Introduction to Generative AI 2. Generative Adversarial Networks 3. Variational Autoencoders 4. Transformer Models and Language Generation 5. Image Generation and Style Transfer 6. Text Generation and Language Models with Real-time Examples 7. Generative AI in Art and Creativity 8. Exploring Advanced Concepts 9. Future Direction and Challenges 10. Building Your Own-Generative AI Models 11. Conclusion and Outlook Appendices
Information And Communication Technologies
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Author : Jorge Maldonado-Mahauad
language : en
Publisher: Springer Nature
Release Date : 2023-10-05
Information And Communication Technologies written by Jorge Maldonado-Mahauad and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-10-05 with Computers categories.
This book constitutes the proceedings of the 11th Ecuadorian Conference on Information and Communication Technologies, TICEC 2023, held in Cuenca, Ecuador, during October 18–20, 2023. The 31 full papers presented were carefully reviewed and selected from 120 submissions. The papers cover a great variety of topics, such as internet of things, cyber-physical systems, human-machine interface, artificial Intelligence, e-Learning, smart healthcare, smart healthcare and others. The papers are organized in the following topical sections: data science and machine learning; ICTs and their applications; and software development.
Python And R For The Modern Data Scientist
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Author : Rick J. Scavetta
language : en
Publisher: "O'Reilly Media, Inc."
Release Date : 2021-06-22
Python And R For The Modern Data Scientist written by Rick J. Scavetta 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 2021-06-22 with Computers categories.
Success in data science depends on the flexible and appropriate use of tools. That includes Python and R, two of the foundational programming languages in the field. This book guides data scientists from the Python and R communities along the path to becoming bilingual. By recognizing the strengths of both languages, you'll discover new ways to accomplish data science tasks and expand your skill set. Authors Rick Scavetta and Boyan Angelov explain the parallel structures of these languages and highlight where each one excels, whether it's their linguistic features or the powers of their open source ecosystems. You'll learn how to use Python and R together in real-world settings and broaden your job opportunities as a bilingual data scientist. Learn Python and R from the perspective of your current language Understand the strengths and weaknesses of each language Identify use cases where one language is better suited than the other Understand the modern open source ecosystem available for both, including packages, frameworks, and workflows Learn how to integrate R and Python in a single workflow Follow a case study that demonstrates ways to use these languages together
Deep Generative Models And Data Augmentation Labelling And Imperfections
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Author : Sandy Engelhardt
language : en
Publisher: Springer Nature
Release Date : 2021-09-29
Deep Generative Models And Data Augmentation Labelling And Imperfections written by Sandy Engelhardt and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-09-29 with Computers categories.
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.
Neuro Symbolic Ai
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Author : Alexiei Dingli
language : en
Publisher: Packt Publishing Ltd
Release Date : 2023-05-31
Neuro Symbolic Ai written by Alexiei Dingli and has been published by Packt Publishing Ltd this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-05-31 with Computers categories.
Explore the inner workings of AI along with its limitations and future developments and create your first transparent and trustworthy neuro-symbolic AI system Purchase of the print or Kindle book includes a free PDF eBook Key Features Understand symbolic and statistical techniques through examples and detailed explanations Explore the potential of neuro-symbolic AI for future developments using case studies Discover the benefits of combining symbolic AI with modern neural networks to build transparent and high-performance AI solutions Book Description Neuro-symbolic AI offers the potential to create intelligent systems that possess both the reasoning capabilities of symbolic AI along with the learning capabilities of neural networks. This book provides an overview of AI and its inner mechanics, covering both symbolic and neural network approaches. You'll begin by exploring the decline of symbolic AI and the recent neural network revolution, as well as their limitations. The book then delves into the importance of building trustworthy and transparent AI solutions using explainable AI techniques. As you advance, you'll explore the emerging field of neuro-symbolic AI, which combines symbolic AI and modern neural networks to improve performance and transparency. You'll also learn how to get started with neuro-symbolic AI using Python with the help of practical examples. In addition, the book covers the most promising technologies in the field, providing insights into the future of AI. Upon completing this book, you will acquire a profound comprehension of neuro-symbolic AI and its practical implications. Additionally, you will cultivate the essential abilities to conceptualize, design, and execute neuro-symbolic AI solutions. What you will learn Gain an understanding of the intuition behind neuro-symbolic AI Determine the correct uses that can benefit from neuro-symbolic AI Differentiate between types of explainable AI techniques Think about, design, and implement neuro-symbolic AI solutions Create and fine-tune your first neuro-symbolic AI system Explore the advantages of fusing symbolic AI with modern neural networks in neuro-symbolic AI systems Who this book is for This book is ideal for data scientists, machine learning engineers, and AI enthusiasts who want to explore the emerging field of neuro-symbolic AI and discover how to build transparent and trustworthy AI solutions. A basic understanding of AI concepts and familiarity with Python programming are needed to make the most of this book.
The Software Principles Of Design For Data Modeling
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Author : Samanta, Debabrata
language : en
Publisher: IGI Global
Release Date : 2023-09-12
The Software Principles Of Design For Data Modeling written by Samanta, Debabrata and has been published by IGI Global this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-09-12 with Computers categories.
The Software Principles of Design for Data Modeling, written by Debabrata Samanta of Rochester Institute of Technology in Kosovo, offers a practical and comprehensive solution to the challenges of designing effective software architecture for data modeling. This book covers key topics such as gathering requirements, modeling requirements with use cases, testing the system, building entity-relationship models, building class models in UML with patterns of data modeling and software quality attributes, and use case modeling. It also includes case studies of relational and object-relational database schema design. The unique approach of this book lies in its unifying method for designing software architecture for data modeling. It addresses specific design issues for various types of software systems, including object-oriented, client/server, service-oriented, component-based, real-time, and software product line architectures. With its practical guidance, standard method for modeling requirements and analysis, and comprehensive coverage of key topics and case studies, this book is a must-read for anyone interested in designing effective software architecture for data modeling, whether you are an academic scholar or a professional in the field.
Synthetic Data Generation
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Author : Robert Johnson
language : en
Publisher: HiTeX Press
Release Date : 2024-10-27
Synthetic Data Generation written by Robert Johnson and has been published by HiTeX Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-10-27 with Computers categories.
"Synthetic Data Generation: A Beginner’s Guide" offers an insightful exploration into the emerging field of synthetic data, essential for anyone navigating the complexities of data science, artificial intelligence, and technology innovation. This comprehensive guide demystifies synthetic data, presenting a detailed examination of its core principles, techniques, and prospective applications across diverse industries. Designed with accessibility in mind, it equips beginners and seasoned practitioners alike with the necessary knowledge to leverage synthetic data's potential effectively. Delving into the nuances of data sources, generation techniques, and evaluation metrics, this book serves as a practical roadmap for mastering synthetic data. Readers will gain a robust understanding of the advantages and limitations, ethical considerations, and privacy concerns associated with synthetic data usage. Through real-world examples and industry insights, the guide illuminates the transformative role of synthetic data in enhancing innovation while safeguarding privacy. With an eye on both present applications and future trends, "Synthetic Data Generation: A Beginner’s Guide" prepares readers to engage with the evolving challenges and opportunities in data-centric fields. Whether for academic enrichment, professional development, or as a primer for new data enthusiasts, this book stands as an essential resource in understanding and implementing synthetic data solutions.
Advanced Deep Learning Strategies For The Analysis Of Remote Sensing Images
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Author : Yakoub Bazi
language : en
Publisher: MDPI
Release Date : 2021-06-15
Advanced Deep Learning Strategies For The Analysis Of Remote Sensing Images written by Yakoub Bazi and has been published by MDPI this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-06-15 with Science categories.
The rapid growth of the world population has resulted in an exponential expansion of both urban and agricultural areas. Identifying and managing such earthly changes in an automatic way poses a worth-addressing challenge, in which remote sensing technology can have a fundamental role to answer—at least partially—such demands. The recent advent of cutting-edge processing facilities has fostered the adoption of deep learning architectures owing to their generalization capabilities. In this respect, it seems evident that the pace of deep learning in the remote sensing domain remains somewhat lagging behind that of its computer vision counterpart. This is due to the scarce availability of ground truth information in comparison with other computer vision domains. In this book, we aim at advancing the state of the art in linking deep learning methodologies with remote sensing image processing by collecting 20 contributions from different worldwide scientists and laboratories. The book presents a wide range of methodological advancements in the deep learning field that come with different applications in the remote sensing landscape such as wildfire and postdisaster damage detection, urban forest mapping, vine disease and pavement marking detection, desert road mapping, road and building outline extraction, vehicle and vessel detection, water identification, and text-to-image matching.