Название: Software Testing and User Experience
Автор: Nastaran Nazar Zadeh
Издательство: Toronto Academic Press
Год: 2024
Страниц: 224
Язык: английский
Формат: pdf (true)
Размер: 70.3 MB

This book explores the relationship between software testing and user experience. It provides an overview of the importance of user experience in software testing and the impact it has on software development. The book covers various techniques and strategies for testing software to ensure optimal user experience, including usability testing, accessibility testing, and performance testing. Whether you are a software developer, tester, or UX designer, this book is a valuable resource for improving the quality and user-friendliness of your software applications. Software testing is an essential aspect of the software development process that ensures software applications’ quality, reliability, and functionality. However, more than testing is needed to guarantee a great user experience. Integrating user experience design and testing throughout the entire software development process is crucial to create intuitive, efficient, and enjoyable software applications. This book aims to provide an in-depth understanding of software testing and user experience design and testing. We will explore the different types of software testing, the goals, scope, and history of software testing, and how testing goes beyond just unit testing. We will also delve into the fundamentals of user experience design and usability testing, as well as the process of conducting a test.

Название: Disease Prediction using Machine Learning, Deep Learning and Data Analytics
Автор: Geeta Rani, Vijaypal Singh Dhaka, Pradeep Kumar Tiwari
Издательство: Bentham Books
Год: 2024
Страниц: 196
Язык: английский
Формат: pdf (true)
Размер: 34.8 MB

This book is a comprehensive review of technologies and data in healthcare services. It features a compilation of 10 chapters that inform readers about the recent research and developments in this field. Each chapter focuses on a specific aspect of healthcare services, highlighting the potential impact of technology on enhancing practices and outcomes. The main features of the book include 1) referenced contributions from healthcare and data analytics experts, 2) a broad range of topics that cover healthcare services, and 3) demonstration of Deep Learning techniques for specific diseases. This book invited ideas, proposals, review articles and experimental works from the researchers working in the field. The systematic organization of the research works in the field of applying Machine Learning for disease prediction will be fruitful in providing insights to readers about the existing works and the gaps available in the field. This book is a significant contribution towards providing a detailed study of data analytics algorithms and Machine Learning techniques for disease prediction. The book includes a rigorous review of related literature, methodology for data set preparation, model building, training, and testing the model. It contains a comparative analysis of versatile algorithms applied for making predictions in the challenging arena of medical science and disease prediction. The provides good insight into the topics such as Data Analytics, Machine Learning, Deep Learning, Information Retrieval from medical data, Data Integration, Prediction Models, Medical Data Analysis, Medical Decision Support systems, Federated Learning in Healthcare, and Medical Image Reconstruction. The book is a companion and a must-read, for academicians, people from industries, graduate and post-graduate students, researchers, physicians and for everyone who is involved in the fields of medicine, Data Analytics or Machine Learning directly or indirectly.

Название: Python for Information Professionals: How to Design Practical Applications to Capitalize on the Data Explosion
Автор: Brady D. Lund, Daniel Agbaji, Kossi Dodzi Bissadu, Haihua Chen
Издательство: Rowman & Littlefield Publishers
Год: 2024
Страниц: 173
Язык: английский
Формат: pdf (true), epub
Размер: 10.1 MB

Python for Information Professionals: How to Design Practical Applications to Capitalize on the Data Explosion is an introduction to the Python programming language for library and information professionals with little or no prior experience. As opposed to the many Python books available today that focus on the language only from a general sense, this book is designed specifically for information professionals who are seeking to advance their career prospects or challenge themselves in new ways by acquiring skills within the rapidly expanding field of Data Science. In this book, we endeavor to introduce you to the basics of Python, in addition to some advanced concepts, as well as contextualize this learning by presenting relevant examples for the library and information sciences. We have intentionally designed this book so that it begins with simpler concepts and then builds in complexity, such that you will be able to follow along regardless of where you are in your Python learning. This book can be used as a learning resource for beginners, or a ready reference for those looking for a few pointers to improve their coding abilities.

Название: Integrating Metaheuristics in Computer Vision for Real-World Optimization Problems
Автор: Shubham Mahajan, Kapil Joshi, Amit Kant Pandit
Издательство: Wiley-Scrivener
Год: 2024
Страниц: 353
Язык: английский
Формат: pdf (true)
Размер: 16.2 MB

This book recognizes digital images and video use in fields of surveillance, manufacturing, and agriculture. Organized in two parts, a variety of readers learn about communication, automation, and beginning a career in research and innovation. The goal of the book is to provide research that addresses broad challenges in both theoretical and application aspects of soft computing and Machine Learning in image processing and computer vision. A computer’s vision to comprehend and analyze visual input is achieved via the application of algorithms, methods, and models. Image capture, preprocessing, feature extraction, and classification are some of the procedures that are taken to accomplish this. Using cameras, sensors, or other imaging equipment, image acquisition includes acquiring visual data. The captured pictures are subsequently cleaned up and improved using preprocessing techniques, such as reducing noise, fixing distortions, and altering the color balance. Feature extraction includes detecting essential properties or aspects of the photos, such as edges, corners, textures, or form. CNNs, which are intended to learn from and extract characteristics from enormous volumes of visual input, are frequently used for this purpose. By classifying photos based on their attributes, Machine Learning techniques are used. Support vector machines (SVMs), decision trees, or Deep Learning algorithms like CNNs can all be used for this. In general, the topic of computer vision is complicated, involving a variety of methods and models, and it is ever-evolving as new studies are undertaken and new applications are created.

Название: Machine Learning for Industrial Applications
Автор: Kolla Bhanu Prakash
Издательство: Wiley-Scrivener
Год: 2024
Страниц: 330
Язык: английский
Формат: pdf
Размер: 18.7 MB

The main goal of the book is to provide a comprehensive and accessible guide that empowers readers to understand, apply, and leverage Machine Learning algorithms and techniques effectively in real-world scenarios. Welcome to the exciting world of Machine Learning! In recent years, machine learning has rapidly transformed from a niche field within Computer Science to a fundamental technology shaping various aspects of our lives. Whether you realize it or not, Machine Learning algorithms are at work behind the scenes, powering recommendation systems, autonomous vehicles, virtual assistants, medical diagnostics, and much more. This book is designed to serve as your comprehensive guide to understanding the principles, algorithms, and applications of Machine Learning. Whether a student diving into this field for the first time, a seasoned professional looking to broaden your skillset, or an enthusiast eager to explore cutting-edge advancements, this book has something for you. The primary goal of Machine Learning for Industrial Applications is to demystify Machine Learning and make it accessible to a wide audience. It provides a solid foundation in the fundamental concepts of Machine Learning, covering both the theoretical underpinnings and practical applications. Whether you’re interested in supervised learning, unsupervised learning, reinforcement learning, or innovative techniques like Deep Learning, you’ll find comprehensive coverage here. Throughout the book, a hands-on approach is emphasized. As the best way to learn Machine Learning is by doing, the book includes numerous examples, exercises, and real-world case studies to reinforce your understanding and practical skills. The book will enjoy a wide readership as it will appeal to all researchers, students, and technology enthusiasts wanting a hands-on guide to the new advances in Machine Learning.

Название: Effective Python: 125 Specific Ways to Write Better Python, 3rd Edition (Early Release)
Автор: Brett Slatkin
Издательство: Addison-Wesley Professional/Pearson Education
Год: 2024
Страниц: 1154
Язык: английский
Формат: pdf, epub, mobi
Размер: 10.1 MB

Master the art of Python programming with 125 actionable best practices to write more efficient, readable, and maintainable code. Python is a versatile and powerful language, but leveraging its full potential requires more than just knowing the syntax. Effective Python: 125 Specific Ways to Write Better Python, 3rd Edition is your comprehensive guide to mastering Python's unique strengths and avoiding its hidden pitfalls. This updated edition builds on the acclaimed second edition, expanding from 90 to 125 best practices that are essential for writing high-quality Python code. Drawing on years of experience at Google, Brett Slatkin offers clear, concise, and practical advice for both new and experienced Python developers. Each item in the book provides insight into the "Pythonic" way of programming, helping you understand how to write code that is not only effective but also elegant and maintainable. Whether you're building web applications, analyzing data, writing automation scripts, or training AI models, this book will equip you with the skills to make a significant impact using Python.

Название: Intelligent Computer Mathematics: 17th International Conference, CICM 2024
Автор: Andrea Kohlhase, Laura Kovács
Издательство: Springer
Серия: Lecture Notes in Artificial Intelligence
Год: 2024
Страниц: 367
Язык: английский
Формат: pdf (true), epub
Размер: 50.2 MB

This book constitutes the refereed proceedings of the 17th International Conference on Intelligent Computer Mathematics, CICM 2024, held in Montréal, Québec, Canada, during August 5–9, 2024. The 21 full papers presented were carefully reviewed and selected from 28 submissions. These papers have been categorized into the following sections: AI and LLM; Proof Assistants; Logical Frameworks and Transformations; Knowledge Representation and Certification; Proof Search and Formalization & System Descriptions. The Conference on Intelligent Computer Mathematics (CICM) brings together the many separate communities that have developed theoretical and practical solutions for mathematical applications in Artificial Intelligence, computation, deduction, knowledge management, or user interfaces. This paper explores the potential of leveraging Large Language Models (LLMs) for the tasks of automated annotation and Part-of-Math (POM) tagging of equations. Traditional methods for math term annotation and POM tagging rely heavily on manually crafted rules and limited datasets, which often result in scalability issues and insufficient adaptability to new domains. In contrast, LLMs, with their vast knowledge and advanced natural language understanding capabilities, present a promising alternative. Our methodology involves crafting prompts for LLMs to elicit answers that can be read as key-value pairs where the keys are math terms and the values are the corresponding annotations.

Название: Intermediate Vulkan Programming: Building 3D Graphics, 2nd Edition
Автор: Frahaan Hussain, Kameron Hussain
Издательство: Sonar Publishing
Год: 2024
Страниц: 234
Язык: английский
Формат: pdf, azw3, epub, mobi
Размер: 10.1 MB

Dive deep into the world of 3D graphics with "Intermediate Vulkan Programming: Building 3D Graphics, 2nd Edition." This comprehensive guide is perfect for developers who have a basic understanding of Vulkan and are looking to enhance their skills. This edition provides an updated and thorough exploration of Vulkan's powerful capabilities, helping you create stunning 3D graphics with precision and efficiency. The book covers advanced techniques and best practices for leveraging the Vulkan API, including memory management, synchronization, and multi-threading. Detailed explanations and practical examples will guide you through the process of building a fully functional 3D engine. You will learn how to handle complex rendering tasks, optimize performance, and create immersive graphical experiences. One of the key features of Vulkan is its ability to handle multiple threads efficiently. Traditional graphics APIs like OpenGL are typically single-threaded, which can become a bottleneck in multi-core systems. Vulkan, however, is designed from the ground up to leverage multi-threading, allowing different parts of the application to execute concurrently and utilize all available CPU cores effectively.

Название: Quick Start Guide to Large Language Models: Strategies and Best Practices for Using ChatGPT and Other LLMs (Final)
Автор: Sinan Ozdemir
Издательство: Addison-Wesley Professional/Pearson Education
Год: 2024
Страниц: 281
Язык: английский
Формат: True/Retail (PDF EPUB)
Размер: 43.2 MB

The Practical, Step-by-Step Guide to Using LLMs at Scale in Projects and Products. Large Language Models (LLMs) like ChatGPT are demonstrating breathtaking capabilities, but their size and complexity have deterred many practitioners from applying them. InQuick Start Guide to Large Language Models, pioneering data scientist and AI entrepreneur Sinan Ozdemir clears away those obstacles and provides a guide to working with, integrating, and deploying LLMs to solve practical problems. Ozdemir brings together all you need to get started, even if you have no direct experience with LLMs: step-by-step instructions, best practices, real-world case studies, hands-on exercises, and more. Along the way, he shares insights into LLMs' inner workings to help you optimize model choice, data formats, parameters, and performance. You'll find even more resources on the companion website, including sample datasets and code for working with open- and closed-source LLMs such as those from OpenAI (GPT-4 and ChatGPT), Google (BERT, T5, and Bard), EleutherAI (GPT-J and GPT-Neo), Cohere (the Command family), and Meta (BART and the LLaMA family). Who is this book for, you ask? Well, my answer is simple: anyone who shares a curiosity about LLMs, the willing coder, the relentless learner. Whether you’re already entrenched in Machine Learning or you’re on the edge, dipping your toes into this vast ocean, this book is your guide, your map to navigate the waters of LLMs. However, I’ll level with you: To get the most out of this journey, having some experience with Machine Learning and Python will be incredibly beneficial.

Название: The LLM Mesh: A Practical Guide to Using Generative AI in the Enterprise (Early Release)
Автор: Kurt Muehmel
Издательство: O’Reilly Media, Inc.
Год: 2024-08-02
Язык: английский
Формат: pdf, azw3, epub, mobi
Размер: 10.1 MB

In the rapidly evolving landscape of Generative AI and particularly large language models (LLMs), organizations are poised to create groundbreaking applications. However, the lack of oversight, governance, and centralization often stymies the deployment of LLM-based applications. For organizations eager to harness the full potential of LLMs, overcoming these hurdles is essential—not only for operational success but also to stay ahead in a competitive market. This tech guide introduces the LLM Mesh, an architecture paradigm that ensures modular development, centralized administration, and auditing of LLM applications. It allows companies not only to keep pace with rapid changes and new market entrants but also to maintain independence from any single provider. Through detailed instructions and expert advice, learn how to abstract applications from LLM services, integrate robust security measures, manage compliance, and optimize costs and performance across various platforms.