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2023 | Buch

Introduction to Generative AI Kapitel lesen Erstes Kapitel lesen

Applied Generative AI for Beginners

Practical Knowledge on Diffusion Models, ChatGPT, and Other LLMs

verfasst von: Akshay Kulkarni, Adarsha Shivananda, Anoosh Kulkarni, Dilip Gudivada

Verlag: Apress

Enthalten in: Springer Professional "Wirtschaft+Technik" , Springer Professional "Technik" , Springer Professional "Wirtschaft"

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SUCHEN

Über dieses Buch

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.

What You Will Learn

Gain a solid understanding of generative AI, starting from the basics of neural networks and progressing to complex architectures like ChatGPT and Google BardImplement large language models using Sklearn, complete with code examples and best practices for real-world applicationLearn how to integrate LLM’s in enterprises, including aspects like LLMOps and technology stack selectionUnderstand how generative AI can be applied across various industries, from healthcare and marketing to legal compliance through detailed use cases and actionable insights

Who This Book Is For

Data scientists, AI practitioners, Researchers and software engineers interested in generative AI and LLMs.

Inhaltsverzeichnis

Frontmatter
Chapter 1. Introduction to Generative AI
Abstract
Have you ever imagined that simply by picturing something and typing, an image or video could be generated? How fascinating is that? This concept, once relegated to the realm of science fiction, has become a tangible reality in our modern world. The idea that our thoughts and words can be transformed into visual content is not only captivating but a testament to human innovation and creativity.
Akshay Kulkarni, Adarsha Shivananda, Anoosh Kulkarni, Dilip Gudivada
Chapter 2. Evolution of Neural Networks to Large Language Models
Abstract
Over the past few decades, language models have undergone significant advancements. Initially, basic language models were employed for tasks such as speech recognition, machine translation, and information retrieval. These early models were constructed using statistical methods, like n-gram and hidden Markov models. Despite their utility, these models had limitations in terms of accuracy and scalability.
Akshay Kulkarni, Adarsha Shivananda, Anoosh Kulkarni, Dilip Gudivada
Chapter 3. LLMs and Transformers
Abstract
In this chapter, we embark on an enlightening journey into the world of LLMs and the intricacies of the Transformer architecture, unraveling the mysteries behind their extraordinary capabilities. These pioneering advancements have not only propelled the field of NLP to new heights but have also revolutionized how machines perceive, comprehend, and generate language.
Akshay Kulkarni, Adarsha Shivananda, Anoosh Kulkarni, Dilip Gudivada
Chapter 4. The ChatGPT Architecture: An In-Depth Exploration of OpenAI’s Conversational Language Model
Abstract
In recent years, significant advancements in natural language processing (NLP) have paved the way for more interactive and humanlike conversational agents. Among these groundbreaking developments is ChatGPT, an advanced language model created by OpenAI. ChatGPT is based on the GPT (Generative Pre-trained Transformer) architecture and is designed to engage in dynamic and contextually relevant conversations with users.
Akshay Kulkarni, Adarsha Shivananda, Anoosh Kulkarni, Dilip Gudivada
Chapter 5. Google Bard and Beyond
Abstract
Google Bard represents a significant advancement in the field of large language models (LLMs). Created by Google AI, this chatbot is the result of training on an extensive corpus of text and code. Its capabilities encompass text generation, language translation, creative content composition, and responsive question answering in an informative way.
Akshay Kulkarni, Adarsha Shivananda, Anoosh Kulkarni, Dilip Gudivada
Chapter 6. Implement LLMs Using Sklearn
Abstract
Scikit-LLM represents a groundbreaking advancement in the realm of text analysis. This innovative tool seamlessly merges the capabilities of robust language models like ChatGPT with the versatile functionality of scikit-learn. The result is an unparalleled toolkit that empowers users to delve into textual data as never before.
Akshay Kulkarni, Adarsha Shivananda, Anoosh Kulkarni, Dilip Gudivada
Chapter 7. LLMs for Enterprise and LLMOps
Abstract
In this chapter, we are presenting a reference framework for the emerging app stack of large language models (LLMs). The framework illustrates the prevalent systems, tools, and design approaches that have been observed in practice among AI startups and enterprises. It's important to note that this stack is in its nascent stages and is likely to undergo significant transformations with the progression of underlying technology. Nevertheless, our intention is for this resource to provide valuable guidance to developers who are presently engaged with LLMs.
Akshay Kulkarni, Adarsha Shivananda, Anoosh Kulkarni, Dilip Gudivada
Chapter 8. Diffusion Model and Generative AI for Images
Abstract
The two prominent generative models, namely, generative adversarial networks (GANs) and variational autoencoders (VAEs), have gained substantial recognition. We will see a brief explanation of both in this chapter followed by a detailed diffusion model. GANs have exhibited versatility across various applications, yet their training complexity and limited output diversity, caused by challenges like mode collapse and gradient vanishing, have been evident. On the other hand, VAEs, while having a strong theoretical foundation, encounter difficulties in devising effective loss functions, resulting in suboptimal outputs.
Akshay Kulkarni, Adarsha Shivananda, Anoosh Kulkarni, Dilip Gudivada
Chapter 9. ChatGPT Use Cases
Abstract
In the era of GenAI, ChatGPT stands as a remarkable and versatile tool with myriad applications across diverse domains. From transforming the landscape of business and customer service to revolutionizing content creation, marketing strategies, and language and communication tasks, ChatGPT's capabilities transcend traditional boundaries. It plays a pivotal role in software development, healthcare, market research, creative writing, education, legal compliance, HR functions, and data analysis, demonstrating its immense potential in shaping the way we approach complex challenges and decision-making across various sectors. This exploration delves into the multifaceted use cases of ChatGPT across different domains, shedding light on its remarkable adaptability and impact.
Akshay Kulkarni, Adarsha Shivananda, Anoosh Kulkarni, Dilip Gudivada
Backmatter
Metadaten
Titel
Applied Generative AI for Beginners
verfasst von
Akshay Kulkarni
Adarsha Shivananda
Anoosh Kulkarni
Dilip Gudivada
Copyright-Jahr
2023
Verlag
Apress
Electronic ISBN
978-1-4842-9994-4
Print ISBN
978-1-4842-9993-7
DOI
https://doi.org/10.1007/978-1-4842-9994-4

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