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Applied generative AI for beginners ...

Kulkarni, Akshay.

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Applied generative AI for beginners = practical knowledge on diffusion models, ChatGPT, and other LLMs /

Record Type:	Electronic resources : Monograph/item
Title/Author:	Applied generative AI for beginners/ by Akshay Kulkarni ... [et al.].
Reminder of title:	practical knowledge on diffusion models, ChatGPT, and other LLMs /
other author:	Kulkarni, Akshay.
Published:	Berkeley, CA :Apress : : 2023.,
Description:	xvi, 212 p. :illustrations, digital ;24 cm.
[NT 15003449]:	Chapter 1: Introduction to Generative AI -- Chapter 2: The Evolution of Neural Networks to Large Language Models -- Chapter 3: LLMs and Transformers -- Chapter 4: The ChatGPT Architecture: An In-Depth Exploration of OpenAI's Conversational Language Model -- Chapter 5: Google Bard and Beyond. - Chapter 6: Implement LLM' using Sklearn -- Chapter 7: LLMs for Enterprise and LLMOps 8: Diffusion Model & Generative AI for Images. - Chapter 9: ChatGTP Use Cases.
Contained By:	Springer Nature eBook
Subject:	Artificial intelligence. -
Online resource:	https://doi.org/10.1007/978-1-4842-9994-4
ISBN:	9781484299944

Applied generative AI for beginners = practical knowledge on diffusion models, ChatGPT, and other LLMs /
Applied generative AI for beginnerspractical knowledge on diffusion models, ChatGPT, and other LLMs /[electronic resource] :by Akshay Kulkarni ... [et al.]. - Berkeley, CA :Apress :2023. - xvi, 212 p. :illustrations, digital ;24 cm.

Chapter 1: Introduction to Generative AI -- Chapter 2: The Evolution of Neural Networks to Large Language Models -- Chapter 3: LLMs and Transformers -- Chapter 4: The ChatGPT Architecture: An In-Depth Exploration of OpenAI's Conversational Language Model -- Chapter 5: Google Bard and Beyond. - Chapter 6: Implement LLM' using Sklearn -- Chapter 7: LLMs for Enterprise and LLMOps 8: Diffusion Model & Generative AI for Images. - Chapter 9: ChatGTP Use Cases.

This book provides a deep dive into the world of generative AI, covering everything from the basics of neural networks to the intricacies of large language models like ChatGPT and Google Bard. It serves as a one-stop resource for anyone interested in understanding and applying this transformative technology and is particularly aimed at those just getting started with generative AI. Applied Generative AI for Beginners is structured around detailed chapters that will guide you from foundational knowledge to practical implementation. It starts with an introduction to generative AI and its current landscape, followed by an exploration of how the evolution of neural networks led to the development of large language models. The book then delves into specific architectures like ChatGPT and Google Bard, offering hands-on demonstrations for implementation using tools like Sklearn. You'll also gain insight into the strategic aspects of implementing generative AI in an enterprise setting, with the authors covering crucial topics such as LLMOps, technology stack selection, and in-context learning. The latter part of the book explores generative AI for images and provides industry-specific use cases, making it a comprehensive guide for practical application in various domains. Whether you're a data scientist looking to implement advanced models, a business leader aiming to leverage AI for enterprise growth, or an academic interested in cutting-edge advancements, this book offers a concise yet thorough guide to mastering generative AI, balancing theoretical knowledge with practical insights. You will: Gain a solid understanding of generative AI, starting from the basics of neural networks and progressing to complex architectures like ChatGPT and Google Bard Implement large language models using Sklearn, complete with code examples and best practices for real-world application Learn how to integrate LLM's in enterprises, including aspects like LLMOps and technology stack selection Understand how generative AI can be applied across various industries, from healthcare and marketing to legal compliance through detailed use cases and actionable insights.

ISBN: 9781484299944

Standard No.: 10.1007/978-1-4842-9994-4doiSubjects--Topical Terms:

516317
Artificial intelligence.

LC Class. No.: Q335

Dewey Class. No.: 006.3

Applied generative AI for beginners = practical knowledge on diffusion models, ChatGPT, and other LLMs /
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245 0 0 $a Applied generative AI for beginners $h [electronic resource] : $b practical knowledge on diffusion models, ChatGPT, and other LLMs / $c by Akshay Kulkarni ... [et al.].
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505 0 $a Chapter 1: Introduction to Generative AI -- Chapter 2: The Evolution of Neural Networks to Large Language Models -- Chapter 3: LLMs and Transformers -- Chapter 4: The ChatGPT Architecture: An In-Depth Exploration of OpenAI's Conversational Language Model -- Chapter 5: Google Bard and Beyond. - Chapter 6: Implement LLM' using Sklearn -- Chapter 7: LLMs for Enterprise and LLMOps 8: Diffusion Model & Generative AI for Images. - Chapter 9: ChatGTP Use Cases.
520 $a This book provides a deep dive into the world of generative AI, covering everything from the basics of neural networks to the intricacies of large language models like ChatGPT and Google Bard. It serves as a one-stop resource for anyone interested in understanding and applying this transformative technology and is particularly aimed at those just getting started with generative AI. Applied Generative AI for Beginners is structured around detailed chapters that will guide you from foundational knowledge to practical implementation. It starts with an introduction to generative AI and its current landscape, followed by an exploration of how the evolution of neural networks led to the development of large language models. The book then delves into specific architectures like ChatGPT and Google Bard, offering hands-on demonstrations for implementation using tools like Sklearn. You'll also gain insight into the strategic aspects of implementing generative AI in an enterprise setting, with the authors covering crucial topics such as LLMOps, technology stack selection, and in-context learning. The latter part of the book explores generative AI for images and provides industry-specific use cases, making it a comprehensive guide for practical application in various domains. Whether you're a data scientist looking to implement advanced models, a business leader aiming to leverage AI for enterprise growth, or an academic interested in cutting-edge advancements, this book offers a concise yet thorough guide to mastering generative AI, balancing theoretical knowledge with practical insights. You will: Gain a solid understanding of generative AI, starting from the basics of neural networks and progressing to complex architectures like ChatGPT and Google Bard Implement large language models using Sklearn, complete with code examples and best practices for real-world application Learn how to integrate LLM's in enterprises, including aspects like LLMOps and technology stack selection Understand how generative AI can be applied across various industries, from healthcare and marketing to legal compliance through detailed use cases and actionable insights.
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