Название: Deep Learning Models: A Practical Approach for Hands-On Professionals Автор: Jonah Gamba Издательство: Springer Год: 2024 Страниц: 211 Язык: английский Формат: pdf (true), epub Размер: 65.3 MB
This book focuses on and prioritizes a practical approach, minimizing theoretical concepts to deliver algorithms effectively. With Deep Learning emerging as a vibrant field of research and development in numerous industrial applications, there is a pressing need for accessible resources that provide comprehensive examples and quick guidance. Unfortunately, many existing books on the market tend to emphasize theoretical aspects, leaving newcomers scrambling for practical guidance. This book takes a different approach by focusing on practicality while keeping theoretical concepts to a necessary minimum. The book begins by laying a foundation of basic information on Deep Learning, gradually delving into the subject matter to explain and illustrate the limitations of existing algorithms. A dedicated chapter is allocated to evaluating the performance of multiple algorithms on specific datasets, highlighting techniques and strategies that can address real-world challenges when Deep Learning is employed. The Chapter 2 introduce some of the concepts needed to start building Deep Learning models in Python. The chapter starts with basic principles related to data manipulation and ends with explanation on how to set up the modelling environment. It is expected that the reader is familiar with some high-level programming concepts which are very easy to acquire within a short space of time. Deep Learning models mostly deal with vectors and matrices as we know them from linear algebra. This book is designed to equip professionals with the necessary skills to thrive in the active field of Deep Learning, where it has the potential to revolutionize traditional problem-solving approaches.
Successfully integrate practical designs that support the full software lifecycle! Designing Software Architectures, 2nd Edition, introduces a practical, step-by-step methodology for architecture design that any professional software engineer can use, with structured methods supported by reusable chunks of design knowledge and rich case studies that demonstrate how to use the methods. This newly updated edition reflects how contemporary architects are designing systems. You will find new chapters on supporting business agility through API-centric design, deployability, cloud-based solutions, and technical debt in design. This edition places more emphasis on the design of APIs for distributed systems so you can make design decisions in systematic, repeatable, and cost-effective ways. The Attribute-Driven Design method may not have changed since this book’s first printing, but almost everything else about the industry has. This book will help you become a better designer who can help pave the road from mediocrity to excellence and from a craft to a discipline of engineering. In the Chapter 1 we provide an introduction to the topic of software architecture and architecture design. We briefly discuss what architecture is and why it is fundamental to take it into account when developing software systems. We also discuss the activities that are associated with the development of software architecture so that architectural design—which is the primary topic of this book—can be understood in the context of these activities. We also briefly discuss the role of the architect, who is the person responsible for creating the design. Finally, we introduce the Attribute-Driven Design (ADD) method, the architecture design method that we will discuss extensively in this book.
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.
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