Generative AI Foundations in Python. (Record no. 16026)
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| 005 - DATE AND TIME OF LATEST TRANSACTION | |
| control field | 20250710182906.0 |
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| fixed length control field | 250616s||||||||||||||||o||||||||||| |d |
| 024 80 - OTHER STANDARD IDENTIFIER | |
| Standard number or code | 9781835464915 |
| 040 ## - CATALOGING SOURCE | |
| Original cataloging agency | PACKT |
| Transcribing agency | PACKT |
| 041 ## - LANGUAGE CODE | |
| Language code of text/sound track or separate title | en |
| 044 ## - COUNTRY OF PUBLISHING/PRODUCING ENTITY CODE | |
| MARC country code | GB |
| 100 0# - MAIN ENTRY--PERSONAL NAME | |
| Personal name | Carlos Rodriguez |
| Relator term | author. |
| 245 00 - TITLE STATEMENT | |
| Title | Generative AI Foundations in Python. |
| 260 ## - PUBLICATION, DISTRIBUTION, ETC. | |
| Place of publication, distribution, etc. | GB: |
| Name of publisher, distributor, etc. | Packt, |
| Date of publication, distribution, etc. | 2024-07-26. |
| 263 ## - PROJECTED PUBLICATION DATE | |
| Projected publication date | 2024-07-26 |
| 264 #1 - PRODUCTION, PUBLICATION, DISTRIBUTION, MANUFACTURE, AND COPYRIGHT NOTICE | |
| Place of production, publication, distribution, manufacture | GB: |
| Name of producer, publisher, distributor, manufacturer | Packt, |
| 300 ## - PHYSICAL DESCRIPTION | |
| Extent | 190. |
| 377 ## - ASSOCIATED LANGUAGE | |
| Language code | en |
| 520 ## - SUMMARY, ETC. | |
| Summary, etc. | <p><b>Begin your generative AI journey with Python as you explore large language models, understand responsible generative AI practices, and apply your knowledge to real-world applications through guided tutorials</b></p><h4>Key Features</h4><ul><li>Gain expertise in prompt engineering, LLM fine-tuning, and domain adaptation</li><li>Use transformers-based LLMs and diffusion models to implement AI applications</li><li>Discover strategies to optimize model performance, address ethical considerations, and build trust in AI systems</li><li>Purchase of the print or Kindle book includes a free PDF eBook</li></ul><h4>Book Description</h4>The intricacies and breadth of generative AI (GenAI) and large language models can sometimes eclipse their practical application. It is pivotal to understand the foundational concepts needed to implement generative AI. This guide explains the core concepts behind -of-the-art generative models by combining theory and hands-on application. Generative AI Foundations in Python begins by laying a foundational understanding, presenting the fundamentals of generative LLMs and their historical evolution, while also setting the stage for deeper exploration. You'll also understand how to apply generative LLMs in real-world applications. The book cuts through the complexity and offers actionable guidance on deploying and fine-tuning pre-trained language models with Python. Later, you'll delve into topics such as task-specific fine-tuning, domain adaptation, prompt engineering, quantitative evaluation, and responsible AI, focusing on how to effectively and responsibly use generative LLMs. By the end of this book, you'll be well-versed in applying generative AI capabilities to real-world problems, confidently navigating its enormous potential ethically and responsibly.<h4>What you will learn</h4><ul><li>Discover the fundamentals of GenAI and its foundations in NLP</li><li>Dissect foundational generative architectures including GANs, transformers, and diffusion models</li><li>Find out how to fine-tune LLMs for specific NLP tasks</li><li>Understand transfer learning and fine-tuning to facilitate domain adaptation, including fields such as finance</li><li>Explore prompt engineering, including in-context learning, templatization, and rationalization through chain-of-thought and RAG</li><li>Implement responsible practices with generative LLMs to minimize bias, toxicity, and other harmful outputs</li></ul><h4>Who this book is for</h4>This book is for developers, data scientists, and machine learning engineers embarking on projects driven by generative AI. A general understanding of machine learning and deep learning, as well as some proficiency with Python, is expected. |
| 538 ## - SYSTEM DETAILS NOTE | |
| System details note | Data in extended ASCII character set. |
| 538 ## - SYSTEM DETAILS NOTE | |
| System details note | Mode of access: Internet. |
| 700 0# - ADDED ENTRY--PERSONAL NAME | |
| Personal name | Samira Shaikh |
| Relator term | author. |
| 710 2# - ADDED ENTRY--CORPORATE NAME | |
| Corporate name or jurisdiction name as entry element | PACKT |
| 773 0# - HOST ITEM ENTRY | |
| Title | Generative AI Foundations in Python |
| Place, publisher, and date of publication | GB,Packt,2024-07-26 |
| Physical description | 190 |
| 856 40 - ELECTRONIC LOCATION AND ACCESS | |
| Uniform Resource Identifier | <a href="https://learning.packt.com/product/475665">https://learning.packt.com/product/475665</a> |
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