Generative large language models von singh ajit (9 Ergebnisse)

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Zustand: As New. Unread book in perfect condition.

Zustand: New.

Zustand: As New. Unread book in perfect condition.

Paperback. Zustand: new. Paperback. "Generative AI and Large Language Models" is a comprehensive, practical, and implementation-focused guide designed for university students and aspiring professionals in the field of computer science. It serves as both a textbook and a hands-on manual, systematically equipping readers with the knowledge and skills required to build applications powered by the most advanced artificial intelligence models available today. Philosophy The core philosophy of this book is "Learning by Doing." In the rapidly advancing field of Generative AI, theoretical knowledge alone is insufficient. True mastery comes from practical application-from writing code, interacting with APIs, fine-tuning models, and deploying solutions. Therefore, this book intentionally minimizes dense theoretical exposition and maximizes hands-on, step-by-step implementation. Every concept is introduced with the immediate goal of applying it. The text treats Generative AI not as an abstract subject to be studied, but as a powerful tool to be wielded. Simplicity is paramount; complex algorithms and architectures are broken down into digestible, numbered steps, making them accessible even to those new to the field. Key Features 1. Globally Relevant: The focus on fundamental principles and industry-standard tools (like Python, LangChain, and major cloud platforms) makes it directly applicable to computer science curricula at universities worldwide. 2. Strictly Implementation-Oriented: The primary focus is on the "how-to." Readers will spend most of their time writing code and building applications, not just reading theory. 3. Step-by-Step Algorithms: Complex processes are presented as simple, numbered algorithms, making them easy to understand, follow, and implement. 4. Updated and Relevant Content: The book covers the latest advancements, including transformer architectures, popular LLMs, vector databases, and deployment strategies, ensuring readers learn current and future-proof skills. 5. End-to-End Project Development: The final chapter provides a complete guide to building and deploying a live Generative AI application, offering invaluable practical experience. Key Takeaways Upon completing this book, the reader will be able to: 1. Understand the fundamental concepts, architecture, and functioning of Generative AI and LLMs. 2. Write effective prompts to control and steer the output of large language models. 3. Utilize popular frameworks like LangChain or LlamaIndex to build complex, multi-step AI applications. 4. Implement advanced techniques such as Retrieval-Augmented Generation (RAG) to connect LLMs with external data. 5. Fine-tune pre-trained models on custom datasets to improve performance for specific tasks. 6. Design, develop, and deploy a complete, end-to-end Generative AI application from scratch. 7. Evaluate the ethical considerations and future scope of Generative AI technologies. Disclaimer: Earnest request from the Author. Kindly go through the table of contents and refer kindle edition for a glance on the related contents. Thank you for your kind consideration! This item is printed on demand. Shipping may be from multiple locations in the US or from the UK, depending on stock availability. …

PAP. Zustand: New. New Book. Shipped from UK. THIS BOOK IS PRINTED ON DEMAND. Established seller since 2000.

Zustand: New. Print on Demand.

PAP. Zustand: New. New Book. Delivered from our UK warehouse in 4 to 14 business days. THIS BOOK IS PRINTED ON DEMAND. Established seller since 2000.

Paperback. Zustand: new. Paperback. "Generative AI and Large Language Models" is a comprehensive, practical, and implementation-focused guide designed for university students and aspiring professionals in the field of computer science. It serves as both a textbook and a hands-on manual, systematically equipping readers with the knowledge and skills required to build applications powered by the most advanced artificial intelligence models available today. Philosophy The core philosophy of this book is "Learning by Doing." In the rapidly advancing field of Generative AI, theoretical knowledge alone is insufficient. True mastery comes from practical application-from writing code, interacting with APIs, fine-tuning models, and deploying solutions. Therefore, this book intentionally minimizes dense theoretical exposition and maximizes hands-on, step-by-step implementation. Every concept is introduced with the immediate goal of applying it. The text treats Generative AI not as an abstract subject to be studied, but as a powerful tool to be wielded. Simplicity is paramount; complex algorithms and architectures are broken down into digestible, numbered steps, making them accessible even to those new to the field. Key Features 1. Globally Relevant: The focus on fundamental principles and industry-standard tools (like Python, LangChain, and major cloud platforms) makes it directly applicable to computer science curricula at universities worldwide. 2. Strictly Implementation-Oriented: The primary focus is on the "how-to." Readers will spend most of their time writing code and building applications, not just reading theory. 3. Step-by-Step Algorithms: Complex processes are presented as simple, numbered algorithms, making them easy to understand, follow, and implement. 4. Updated and Relevant Content: The book covers the latest advancements, including transformer architectures, popular LLMs, vector databases, and deployment strategies, ensuring readers learn current and future-proof skills. 5. End-to-End Project Development: The final chapter provides a complete guide to building and deploying a live Generative AI application, offering invaluable practical experience. Key Takeaways Upon completing this book, the reader will be able to: 1. Understand the fundamental concepts, architecture, and functioning of Generative AI and LLMs. 2. Write effective prompts to control and steer the output of large language models. 3. Utilize popular frameworks like LangChain or LlamaIndex to build complex, multi-step AI applications. 4. Implement advanced techniques such as Retrieval-Augmented Generation (RAG) to connect LLMs with external data. 5. Fine-tune pre-trained models on custom datasets to improve performance for specific tasks. 6. Design, develop, and deploy a complete, end-to-end Generative AI application from scratch. 7. Evaluate the ethical considerations and future scope of Generative AI technologies. Disclaimer: Earnest request from the Author. Kindly go through the table of contents and refer kindle edition for a glance on the related contents. Thank you for your kind consideration! This item is printed on demand. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability. …