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Titlebook: Applied Generative AI for Beginners; Practical Knowledge Akshay Kulkarni,Adarsha Shivananda,Dilip Gudivada Book 2023 Akshay Kulkarni, Adar

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發(fā)表于 2025-3-21 18:00:04 | 只看該作者 |倒序瀏覽 |閱讀模式
期刊全稱Applied Generative AI for Beginners
期刊簡稱Practical Knowledge
影響因子2023Akshay Kulkarni,Adarsha Shivananda,Dilip Gudivada
視頻videohttp://file.papertrans.cn/160/159832/159832.mp4
發(fā)行地址Employs a holistic approach, covering evolution, fundamentals, architecture, implementations, and real-world cases.Offers insights into LLMs like ChatGPT and Google Bard.Explains how to implement gene
圖書封面Titlebook: Applied Generative AI for Beginners; Practical Knowledge  Akshay Kulkarni,Adarsha Shivananda,Dilip Gudivada Book 2023 Akshay Kulkarni, Adar
影響因子.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 u
Pindex Book 2023
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發(fā)表于 2025-3-21 21:26:33 | 只看該作者
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978-1-4842-9993-7Akshay Kulkarni, Adarsha Shivananda, Anoosh Kulkarni, Dilip Gudivada 2023
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發(fā)表于 2025-3-22 12:06:32 | 只看該作者
Information-Integrated Enterprises,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.
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發(fā)表于 2025-3-22 15:48:58 | 只看該作者
https://doi.org/10.1007/978-1-4615-2363-5Scikit-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.
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Information-Integrated Enterprises,e 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.
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發(fā)表于 2025-3-23 09:06:54 | 只看該作者
Examples of Information Modeling,peech 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.
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