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Название: The Quick Guide to Prompt Engineering: Generative AI Tips and Tricks for ChatGPT, Bard, Dall-E, and Midjourney
Автор: Ian Khan
Издательство: Wiley
Год: 2024
Страниц: 479
Язык: английский
Формат: pdf (true), epub (true)
Размер: 10.1 MB

Design and use Generative AI prompts that get helpful and practical results in this concise and quick start guide. In The Quick Guide to Prompt Engineering, renowned technology futurist and AI thought leader Ian Khan delivers a practical and insightful resource for taking the first steps in understanding and learning how to use Generative AI. You will learn how to design and use prompts to get the most out of Large Language Model generative AI applications like ChatGPT, DALL-E, Google’s Bard, and explore how to understand Generative Artificial Intelligence and how to engineer prompts in a wide variety of industry use cases. You’ll also find illuminating case studies and hands-on exercises, as well as step-by-step guides, to get you up to speed on prompt engineering in no time at all. The book has been written for the non-technical user to take the first steps in the world of Generative AI. Codex represents not merely the next iteration of code-generation tools but an avant-garde transformation of how we perceive the confluence of coding and AI. At its essence, Codex is a prodigious language model specifically designed for generating code, and its inception can be considered both an evolution and a revolution. Codex's lineage traces back to the landmark achievements of its predecessors, particularly GPT-3, one of Open AI's most lauded creations. While GPT-3 demonstrated awe-inspiring abilities across a wide range of natural language processing tasks, Codex fine-tunes this prowess with an emphasis on code generation.

Название: Large Language Models: An Introduction
Автор: Oswald Campesato
Издательство: Mercury Learning and Information
Год: 2024
Страниц: 542
Язык: английский
Формат: pdf, epub (true), mobi
Размер: 10.1 MB

This book begins with an overview of the Generative AI landscape, distinguishing it from conversational AI and shedding light on the roles of key players like DeepMind and OpenAI. It then reviews the intricacies of ChatGPT, GPT-4, Meta AI, Claude 3, and Gemini, examining their capabilities, strengths, and competitors. Readers will also gain insights into the BERT family of LLMs, including ALBERT, DistilBERT, and XLNet, and how these models have revolutionized natural language processing. Further, the book covers prompt engineering techniques, essential for optimizing the outputs of AI models, and addresses the challenges of working with LLMs, including the phenomenon of hallucinations and the nuances of fine-tuning these advanced models. Designed for software developers, AI researchers, and technology enthusiasts with a foundational understanding of AI, this book offers both theoretical insights and practical code examples in Python. This book is intended primarily for people who have a basic knowledge of Generative AI or software developers who are interested in working with LLMs. Specifically, this book is for readers who are accustomed to searching online for more detailed information about technical topics.

Название: Kickstart Software Design Architecture: Transform Your Software Development Journey with Key Principles and Advanced Practices in Architecture, Microservices, and Cloud Computing
Автор: Edward D Lavieri Jr.
Издательство: Orange Education Pvt Ltd, AVA
Год: 2024
Страниц: 356
Язык: английский
Формат: epub (true)
Размер: 17.9 MB

Learn to design robust software systems using modern architecture principles and practical hands-on experience. Kickstart Software Design Architecture is designed to empower readers with a deep understanding of software architecture's critical role in successful software development. Firstly, the book offers interactive exercises, allowing readers to apply principles in real-world scenarios, thereby solidifying their understanding of how software architecture directly impacts the efficiency, scalability, and maintainability of applications. Secondly, through an extensive collection of case studies and examples, it provides industry's best practices, demonstrating the influence of well-designed software architecture on a project’s success. Explore the ever-evolving world of software architecture. The book highlights the significance of software architecture in building scalable, efficient, and robust applications. The book is structured into engaging chapters, each focused on a specific aspect of software architecture. It starts with an introduction to the basics of software design patterns, gaining an understanding of their role in crafting flexible and reusable code. Next, microservices are covered, followed by chapters that focus on cloud computing, containerization, and more. Chapters contain real-world examples, hands-on exercises, and case studies, to help readers gain both foundational knowledge and hands-on experience. By the end of the book, you should have a solid foundation in software architecture and be equipped with the knowledge and skills to confidently address complex software architectural challenges. The Chapter 3 explores the impact of programming languages on software design by comparing Java and Python, highlighting their unique architectural considerations, and discussing best practices for coding in each language.

Название: Large Language Models Projects: Apply and Implement Strategies for Large Language Models
Автор: Pere Martra
Издательство: Apress
Год: 2024
Страниц: 366
Язык: английский
Формат: pdf
Размер: 12.0 MB

This book offers you a hands-on experience using models from OpenAI and the Hugging Face library. You will use various tools and work on small projects, gradually applying the new knowledge you gain. The book is divided into three parts. Part one covers techniques and libraries. Here, you'll explore different techniques through small examples, preparing to build projects in the next section. You'll learn to use common libraries in the world of Large Language Models. Topics and technologies covered include chatbots, code generation, OpenAI API, Hugging Face, vector databases, LangChain, fine tuning, PEFT fine tuning, soft prompt tuning, LoRA, QLoRA, evaluating models, and Direct Preference Optimization. Part two focuses on projects. You'll create projects, understanding design decisions. Each project may have more than one possible implementation, as there is often not just one good solution. You'll also explore LLMOps-related topics. Part three delves into enterprise solutions. Large Language Models are not a standalone solution; in large corporate environments, they are one piece of the puzzle. You'll explore how to structure solutions capable of transforming organizations with thousands of employees, highlighting the main role that Large Language Models play in these new solutions. For Data analysts, Data Science, Python developers, and software professionals interested in learning the foundations of NLP, LLMs, and the processes of building modern LLM applications for various tasks.

Название: Mathematical Modeling for Computer Applications
Автор: Biswadip Basu Mallik, M. Niranjanamurthy, Sharmistha Ghosh, Valentina Emilia Balas, Krishanu Deyasi, Santanu Das
Издательство: Wiley-Scrivener
Серия: Mathematics and Computer Science
Год: 2024
Страниц: 546
Язык: английский
Формат: pdf (true)
Размер: 46.1 MB

This new volume in this groundbreaking series on mathematics and computer science builds on the basic concepts and fundamentals laid out in previous volumes, presenting the reader with more advanced and cutting-edge topics being developed in this exciting field. Mathematics is the foundation on which Computer Science is built. Mathematical Logic, Set theory, Combinatorics, Graph Theory, Probability Theory, Number Theory, and Algebra are all part of Discrete Mathematics and form the basic foundation for Programming and Computer Science. Analytical skills are a necessity for learning problem-solving and data analysis. And, mathematics is the first place where you get to use these skills. Computer science examines the principles and use of computers in processing information, designing hardware and software, and using applications. Algebra is used in computer science in the development of algorithms and software for working with mathematical objects. It is also used to design formulas that are used in numerical programs and for complete scientific computations. Statistics is a form of mathematics used in Computer Science that uses quantified models, representations, and synopses for a provided collection of experimental data or actual studies. The field studies methodologies to obtain, review, evaluate, and form conclusions from data. Statistics is used for data mining, speech recognition, vision and image analysis, data compression, traffic modelling, and even Artificial Intelligence (AI) and for simulations.

Название: Introduction to Quantitative Social Science with Python
Автор: Weiqi Zhang, Dmitry Zinoviev
Издательство: CRC Press
Серия: The Python Series
Год: 2025
Страниц: 356
Язык: английский
Формат: pdf (true), epub
Размер: 13.1 MB

Departing from traditional methodologies of teaching data analysis, this book presents a dual-track learning experience, with both Executive and Technical Tracks, designed to accommodate readers with various learning goals or skill levels. Through integrated content, readers can explore fundamental concepts in data analysis while gaining hands-on experience with Python programming, ensuring a holistic understanding of theory and practical application in Python. Emphasizing the practical relevance of data analysis in today's world, the book equips readers with essential skills for success in the field. By advocating for the use of Python, an open-source and versatile programming language, we break down financial barriers and empower a diverse range of learners to access the tools they need to excel. Whether you're a novice seeking to grasp the foundational concepts of data analysis or a seasoned professional looking to enhance your programming skills, this book offers a comprehensive and accessible guide to mastering the art and science of data analysis in social science research.

Название: Ransomware Analysis: Knowledge Extraction and Classification for Advanced Cyber Threat Intelligence
Автор: Claudia Lanza, Abdelkader Lahmadi, Jérôme François
Издательство: CRC Press
Год: 2025
Страниц: 113
Язык: английский
Формат: pdf (true), epub
Размер: 10.1 MB

This book presents the development of a classification scheme to organize and represent ransomware threat knowledge through the implementation of an innovative methodology centered around the semantic annotation of domain-specific source documentation. By combining principles from Computer Science, document management, and semantic data processing, the research establishes an innovative framework to organize ransomware data extracted from specialized source texts in a systematic classification system. Through detailed chapters, the book explores the process of applying semantic annotation to a specialized corpus comprising CVE prose descriptions linked to known ransomware threats. This approach not only organizes but also deeply analyzes these descriptions, uncovering patterns and vulnerabilities within ransomware operations. The book presents a pioneering methodology that integrates CVE descriptions with ATT&CK frameworks, significantly refining the granularity of threat intelligence. The insights gained from a pattern-based analysis of vulnerability-related documentation are structured into a hierarchical model within an ontology framework, enhancing the capability for predictive operations. This model prepares cybersecurity professionals to anticipate and mitigate risks associated with new vulnerabilities as they are cataloged in the CVE list, by identifying recurrent characteristics tied to specific ransomware and related vulnerabilities. With real-world examples, this book empowers its readers to implement these methodologies in their environments, leading to improved prediction and prevention strategies in the face of growing ransomware challenges.

Название: Beginning MongoDB Atlas with .NET: Flexible and Scalable Document Storage for .NET Developers
Автор: Luce Carter
Издательство: Apress
Год: 2024
Страниц: 164
Язык: английский
Формат: pdf
Размер: 10.1 MB

This book is a tutorial on MongoDB customized for developers working in Microsoft .NET 6, .NET 7, and beyond. It explains the differences between relational database systems and the document model supported by MongoDB, and shows how to build .NET applications that run against a MongoDB database, especially one in the cloud. Author Luce Carter kicks things off by teaching you how to determine when to use a document database versus a relational engine. After that, she walks you through building a Microsoft .NET project combining the MongoDB Atlas cloud database as a service solution with a .NET. application. In the process, you will learn how to create, read, update, and delete data in MongoDB from any .NET project. You will come away from this book with a solid understanding of MongoDB’s Developer Data Platform and how to use it from your .NET applications. You’ll be able to connect to MongoDB in the cloud and take advantage of the flexibility and scalability that MongoDB’s document storage model provides, and you’ll understand how to craft your applications to run using document storage and the MongoDB database engine. For .NET developers who are looking for an alternative to relational databases, and those looking for a flexible and scalable document storage solution for use from .NET applications. Additionally, anyone wanting to learn MongoDB in the context of .NET and C# will benefit from this book.

Название: Future Tech Startups and Innovation in the Age of AI
Автор: Inam Ullah Khan, Hamed Taherdoost, Mitra Madanchian, Mariya Ouaissa, Salma El Hajjami, Hameedur Rahman
Издательство: CRC Press
Год: 2025
Страниц: 262
Язык: английский
Формат: pdf (true), epub
Размер: 13.7 MB

Our book, Future of Tech Startups and Innovations in the Age of AI, mainly focuses on Artificial Intelligence (AI) tools, AI-based startups, AI-enabled innovations, Autonomous AI Agents (Auto-GPT), AI-based marketing startups, Machine Learning for organizations, AI-internet of things (IoT) for new tech companies, AI-enabled drones for agriculture industry, Machine Learning (ML)/Deep Learning (DL)-based drip farming, AI-based driverless cars, AI-based weather prediction startups, AI tools for personal branding, AI-based teaching, AI-based doctor/hospital startups, AI for game companies, AI-based finance tools, AI for human resource management, AI-powered management tools, AI tools for future pandemics, AI/ML-based transportation companies, AI for media, AI for carrier counseling, AI for customer care, AI for next generation businesses, and many more applications. AI tools and techniques will revolutionize startups all over the world. Entrepreneurs, engineers, and practitioners have already moved toward AI-based solutions to reshape businesses. AI/ML will create possibilities and opportunities for improving human lifestyles. AI-enabled startups will work on cost-effective solutions to solve difficult problems. Recently, many research companies are interested in providing solutions and investing a lot in AI-based startups. AI-driven products will revolutionize the "smart world." AI computing tech companies will help to model human speech recognition systems. Also, AI-based startups will focus on perception and reasoning of autonomous robotic systems.

Название: A Beginner's Guide to Medical Application Development with Deep Convolutional Neural Networks
Автор: Snehan Biswas, Amartya Mukherjee, Nilanjan Dey
Издательство: CRC Press
Год: 2025
Страниц: 199
Язык: английский
Формат: pdf (true), epub
Размер: 51.9 MB

This book serves as a source of introductory material and reference for medical application development and related technologies by providing the detailed implementation of cutting-edge Deep Learning methodologies. It targets cloud-based advanced medical application developments using open-source Python-based Deep Learning libraries. It includes code snippets and sophisticated convolutional neural networks to tackle real-world problems in medical image analysis and beyond. In recent years, deep neural networks (DNNs) have emerged as a powerful tool for analyzing medical data. These networks are capable of learning complex patterns in data, and can be trained to identify subtle features that traditional methods may miss. With the ability to process large amounts of data quickly and accurately, DNNs offer a promising avenue for improving medical diagnosis and treatment. This book aims to provide a comprehensive introduction to the use of DNNs for medical data analysis. The book is intended for a wide audience, including healthcare professionals, data scientists, researchers, and students. It covers the basics of neural networks and Deep Learning, and provides a detailed overview of the various types of networks that can be used for medical data analysis. The book also includes case studies and practical examples of how DNNs have been used to diagnose and treat a range of medical conditions. Chapter 1 provides an overview of the basics of neural networks and Deep Learning. It covers the fundamentals of how neural networks work and how they can be used to learn from data. The chapter also explores different types of Deep Learning architectures and how they can be used for medical data analysis. Chapter 2 focuses on the details about a special type of neural network, known as the convolutional neural network (CNN), and how it can solve the problems of image analysis by learning different features of the training images. The chapter also provides an idea about some of the recent state-of-the-art models that have been developed by researchers all around the world. This book is aimed at graduate students and researchers in medical data analytics, medical image analysis, signal processing, and Deep Learning.