Название: Generative Analysis: The Power of Generative AI for Object-Oriented Software Engineering with UML (Early Release) Автор: Jim Arlow, Ila Neustadt Издательство: Addison-Wesley Professional/Pearson Education Год: 2024 Страниц: 512 Язык: английский Формат: epub (true) Размер: 67.4 MB
Learn a new method of object-oriented analysis called generative analysis and keep your skill-set on pace with how generative AI is transforming the face of software engineering. Generative AI is revolutionizing many industries, including software engineering. Many aspects of manual coding are becoming automated, and the skills needed by software engineers, developers, and analysts are evolving. Anyone who writes or works with code will need to produce precise analysis artifacts to feed the AI code generation process. Enter generative analysis: a precise, structured way to for software engineers, programmers, and analysts to transition to this new, AI-enhanced, software engineering world. In Generative Analysis, experts Jim Arlow and Ila Neustadt leverage literate modeling, M++, and multivalent logic to lay out a precise and structured, step-by-step approach to object-oriented analysis that produces clear and unambiguous results suitable for further processing into code by generative AI systems such as Copilot, ChatGPT, and Gemini. Ideally, you would be a business analyst who is also a competent programmer who is comfortable building models of software systems with the Unified Modeling Language (UML). We understand that this is a very big ask! However, you don’t need to be an expert programmer, you just need to be able to read code and understand UML artefacts without necessarily understanding the fine details. For code examples, we have chosen Python because this is probably the most easily readable programming language.
Название: Innovation in the University 4.0 System based on Smart Technologies Автор: Shashi Kant Gupta, Joanna Rosak-Szyrocka Издательство: CRC Press Год: 2024 Страниц: 241 Язык: английский Формат: pdf (true) Размер: 36.2 MB
This text presents a comprehensive analysis of mathematical formulations for proving the effectiveness of Artificial Intelligence in education and investigates the possibilities for integrating advanced Artificial Intelligence algorithms. Artificial Intelligence (AI) in education signifies a paradigm shift in teaching and learning. A major driving force behind AI is the concept of neural networks and Deep Learning. These powerful tools enable AI applications to deliver personalized, dynamic, and engaging learning experiences. To understand the role of these technologies in education, we must first comprehend the basics. Neural networks are AI systems modeled after the human brain, consisting of interconnected layers of nodes, or “neurons,” that process information. These layers constitute an input layer, one or more hidden layers, and an output layer. Each node processes the input it receives and passes on the result, simulating the process of human brain cells transmitting signals. Deep Learning, a subset of Machine Learning, involves using neural networks with multiple hidden layers. The text is primarily written for graduate students, postgraduate students, and academic researchers working in the fields of Computer Science and engineering, information technology and Machine Learning.
Название: Image Processing Recipes in MATLAB Автор: Oge Marques, Gustavo Benvenutti Borba Издательство: CRC Press Год: 2024 Страниц: 263 Язык: английский Формат: pdf (true) Размер: 32.5 MB
Leveraging the latest developments in MATLAB and its image processing toolbox, this 'cookbook' is a collection of 30 practical recipes for image processing, ranging from foundational techniques to recently published algorithms. Presented in a clear and meaningful sequence, these recipes are prepared with the reader in mind, allowing one to focus on particular topics or read as a whole from cover to cover. This is a cookbook containing 30 recipes that showcase classic and modern image processing techniques using MATLAB. This book aims to provide a concise and easily understandable reference for deploying image processing pipelines quickly and efficiently in MATLAB. The recipes cover the latest developments in MATLAB and relevant toolboxes, including a wide range of image processing methods. These methods range from foundational techniques found in textbooks to popular contemporary algorithms. The book serves as a concise and readable practical reference to deploy image processing pipelines in MATLAB quickly and efficiently. With its accessible and practical approach, the book is a valuable guide for those who navigate this evolving area, including researchers, students, developers, and practitioners in the fields of image processing, computer vision, and image analysis.
Название: A Simple Introduction to Python Автор: Stephen Lynch Издательство: CRC Press Серия: The Python Series Год: 2024 Страниц: 113 Язык: английский Формат: pdf (true) Размер: 21.4 MB
A Simple Introduction to Python is aimed at pre-university students and complete novices to programming. The whole book has been created using Jupyter notebooks. After introducing Python as a powerful calculator, simple programming constructs are covered, and the NumPy, MatPlotLib and SymPy modules (libraries) are introduced. Python is then used for Mathematics, Cryptography, Artificial Intelligence, Data Science and Object Oriented Programming. The reader is shown how to program using the integrated development environments: Python IDLE, Spyder, Jupyter notebooks, and through cloud computing with Google Colab. Currently, Python is the most popular programming language in the world. It is open source and completely free to the user. It is also fun to program with and extremely powerful at solving real-world problems. This book is for pre-university students or complete novices to programming. Each chapter provides both examples and exercises. In order to understand programming, the reader must attempt these exercises. Learning to program is like learning to ride a bike – you have to fall off many times before mastering the art. Expect to make many mistakes, learn from those mistakes and then move on to the next chapter. Full working solutions to all of the exercises and other resources will be provided via GitHub, where readers will be able to download all files for free. There will also be a web page with full-worked solutions, where readers can simply copy and paste code and run the programs in a Python environment. Readers should note that Python programs can easily be generated with ChatGPT, developed by OpenAI, and other alternatives such as Microsoft Bing, Perplexity AI and Google Bard AI, for example.
Название: Supervised and Unsupervised Data Engineering for Multimedia Data Автор: Suman Kumar Swarnkar, J.P. Patra, Sapna Singh Kshatri Издательство: Wiley-Scrivener Год: 2024 Страниц: 372 Язык: английский Формат: pdf, epub Размер: 10.1 MB
Explore the cutting-edge realms of data engineering in multimedia with Supervised and Unsupervised Data Engineering for Multimedia Data, where expert contributors delve into innovative methodologies, offering invaluable insights to empower both novices and seasoned professionals in mastering the art of manipulating multimedia data with precision and efficiency. Supervised and Unsupervised Data Engineering for Multimedia Data presents a groundbreaking exploration into the intricacies of handling multimedia data through the lenses of both supervised and unsupervised data engineering. Authored by a team of accomplished experts in the field, this comprehensive volume serves as a go-to resource for data scientists, computer scientists, and researchers seeking a profound understanding of cutting-edge methodologies. The book seamlessly integrates theoretical foundations with practical applications, offering a cohesive framework for navigating the complexities of multimedia data. Readers will delve into a spectrum of topics, including Artificial Intelligence (AI), Machine Learning, and data analysis, all tailored to the challenges and opportunities presented by multimedia datasets. From foundational principles to advanced techniques, each chapter provides valuable insights, making this book an essential guide for academia and industry professionals alike. Whether you’re a seasoned practitioner or a newcomer to the field, Supervised and Unsupervised Data Engineering for Multimedia Data illuminates the path toward mastery in manipulating and extracting meaningful insights from multimedia data in the modern age.
Название: Build an Orchestrator in Go (From Scratch) (Final Release) Автор: Tim Boring Издательство: Manning Publications Год: 2024 Страниц: 288 Язык: английский Формат: pdf, epub (true), mobi Размер: 10.1 MB
Develop a deep understanding of Kubernetes and other orchestration systems by building your own with Go and the Docker API. Orchestration systems like Kubernetes can seem like a black box: you deploy to the cloud and it magically handles everything you need. That might seem perfect—until something goes wrong and you don’t know how to find and fix your problems. Build an Orchestrator in Go (From Scratch) reveals the inner workings of orchestration frameworks by guiding you through creating your own. Build an Orchestrator in Go (From Scratch) gives you an inside-out perspective on orchestration frameworks and the low-level operation of distributed containerized applications. It takes you on a fascinating journey building a simple-but-useful orchestrator using the Docker API and Go SDK. As you go, you’ll get a guru-level understanding of Kubernetes, along with a pattern you can follow when you need to create your own custom orchestration solutions. Build an Orchestrator in Go (From Scratch) is for anyone responsible for deploying and operating an orchestration system (i.e., DevOps engineers and SREs) and for anyone responsible for deploying and managing applications that run on an orchestration system (i.e., software engineers).
Название: Actin Computation: Unlocking the Potential of Actin Filaments for Revolutionary Computing Systems Автор: Andrew Adamatzky Издательство: World Scientific Publishing Год: 2024 Страниц: 329 Язык: английский Формат: pdf (true) Размер: 20.8 MB
Actin, a fundamental building block of intracellular cytoskeletal filaments, holds immense potential as a platform for information processing. In this groundbreaking book, the authors establish a solid theoretical and modelling foundation for the future experimental prototyping of actin-based cytoskeleton computers in laboratory settings. By delving into the mechanisms of signal propagation and perturbations on actin filaments, the authors uncover the underlying principles of information processing at the sub-cellular level. Through their innovative insights, novel principles for information processing emerge, potentially revolutionising the field of future and emergent information technologies and computational biology. The book's theoretical and modelling framework serves as a crucial stepping stone towards the experimental realisation of actin-based cytoskeleton computers. Bridging the gap between theory and practice, researchers and scientists can leverage this knowledge to conduct experiments, validate hypotheses, and explore the practical implications of actin-based computing systems. Readership: Primary: Computer architects, system developers, molecular biologists. Secondary: Popular computing-interested public.
Название: Intelligent Systems and Sustainable Computational Models: Concepts, Architecture, and Practical Applications Автор: Rajganesh Nagarajan, Senthil Kumar Narayanasamy Издательство: CRC Press Год: 2024 Страниц: 429 Язык: английский Формат: pdf (true) Размер: 20.0 MB
The fields of intelligent systems and sustainability have been gaining momentum in the research community. They have drawn interest in such research fields as Computer Science, information technology, electrical engineering, and other associated engineering disciples. The promise of intelligent systems applied to sustainability is becoming a reality due to the recent advancements in the Internet of Things (IoT), Artificial Intelligence (AI), Big Data, Blockchain, Deep Learning, and Machine Learning. The emergence of intelligent systems has given rise to a wide range of techniques and algorithms using an ensemble approach to implement novel solutions for complex problems associated with sustainability. Intelligent Systems and Sustainable Computational Models: Concepts, Architecture, and Practical Applications explores this ensemble approach towards building a sustainable future. The book helps researchers to apply intelligent systems to computational sustainability models to propose efficient methods, techniques, and tools.
Название: Secrets of Machine Learning: How It Works and What It Means for You Автор: Tom Kohn Издательство: World Scientific Publishing Год: 2024 Страниц: 239 Язык: английский Формат: pdf (true) Размер: 10.5 MB
Cutting through the mass of technical literature on Machine Learning and AI and the plethora of fear-mongering books on the rise of killer robots, Secrets of Machine Learning offers a clear-sighted explanation for the informed reader of what this new technology is, what it does, how it works, and why it's so important.The surge in computer processing power along with the sheer quantities of training data available, means Machine Learning is now possible in ways wholly unthinkable just five years ago. Computers can recognize potential lung cancer better than doctors, detect fraud better than bankers, and create fake video almost impossible to tell from the real thing. And next, they are likely to drive our cars. Journalist and news product manager Tom Kohn gets to the heart of the revolutionary new technology that is developing all around us, explaining with precision how the different facets of Machine Learning work, how companies are using it, and why it is permeating all parts of society right now. The book guides readers through the arcane science and jargon in a clear and understandable way, but is detailed enough that it doesn't gloss over the hard technical concepts. If you want to know why Siri sometimes misunderstands you, how Netflix recommends your movies, and how Machine Learning will affect your job -- read this book. Anyone curious about how technology is changing in the workplace and the economy will benefit from learning how Machine Learning is filtering into different areas. Employees looking to future-proof their own skills, students seeking to get ahead as they enter the job market, and anyone with a general interest in cutting-edge industry.
Название: Metaheuristics for Machine Learning: Algorithms and Applications Автор: Kanak Kalita, Narayanan Ganesh Издательство: Wiley-Scrivener Серия: Artificial Intelligence and Soft Computing for Industrial Transformation Год: 2024 Страниц: 327 Язык: английский Формат: pdf, epub Размер: 10.1 MB
The book unlocks the power of nature-inspired optimization in Machine Learning and presents a comprehensive guide to cutting-edge algorithms, interdisciplinary insights, and real-world applications. The field of metaheuristic optimization algorithms is experiencing rapid growth, both in academic research and industrial applications. These nature-inspired algorithms, which draw on phenomena like evolution, swarm behavior, and neural systems, have shown remarkable efficiency in solving complex optimization problems. With advancements in Machine Learning and Artificial Intelligence, the application of metaheuristic optimization techniques has expanded, demonstrating significant potential in optimizing Machine Learning models, hyperparameter tuning, and feature selection, among other use-cases. In the industrial landscape, these techniques are becoming indispensable for solving real-world problems in sectors ranging from healthcare to cybersecurity and sustainability. Businesses are incorporating metaheuristic optimization into Machine Learning workflows to improve decision-making, automate processes, and enhance system performance. As the boundaries of what is computationally possible continue to expand, the integration of metaheuristic optimization and Machine Learning represents a pioneering frontier in computational intelligence, making this book a timely resource for anyone involved in this interdisciplinary field. Genetic algorithms (GAs) fit to a cluster of metaheuristic optimization techniques that draw inspiration from natural selection and genetics. In order to find the optimal solution for a particular issue, the core idea underlying the GA is to mimic the evolutionary process.
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