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Building generative AI-powered apps = a hands-on guide for developers /
Record Type:
Electronic resources : Monograph/item
Title/Author:
Building generative AI-powered apps/ by Aarushi Kansal.
Reminder of title:
a hands-on guide for developers /
Author:
Kansal, Aarushi.
Published:
Berkeley, CA :Apress : : 2024.,
Description:
xi, 170 p. :ill., digital ;24 cm.
Contained By:
Springer Nature eBook
Subject:
Application software - Development. -
Online resource:
https://doi.org/10.1007/979-8-8688-0205-8
ISBN:
9798868802058
Building generative AI-powered apps = a hands-on guide for developers /
Kansal, Aarushi.
Building generative AI-powered apps
a hands-on guide for developers /[electronic resource] :by Aarushi Kansal. - Berkeley, CA :Apress :2024. - xi, 170 p. :ill., digital ;24 cm.
Generative AI has gone beyond the responsibility of researchers and data scientists and is being used by production engineers. However, there is a lot of confusion where to get started when building an end-to-end app with generative AI. This book consolidates core models, frameworks, and tools into a single source of knowledge. By providing hands-on examples, the book takes you through the generative AI ecosystem to build applications for production. The book starts with a brief and accessible introduction to transformer models before delving into some of the most popular large language models and diffusions models (image generation) These models are the foundations of both AI and your potential new apps. You will then go through various tools available to work with these models, starting with Langchain, a framework to develop foundational models, which is the next building block you should grasp after understanding generative AI models. The next chapters cover databases, caching, monitoring, etc., which are the topics necessary to build larger-scale applications. Real-world examples using these models and tools are included. By the end of this book, you should be able to build end-to-end apps that are powered by generative AI. You also should be able to apply the tools and techniques taught in this book to your use cases and business. What You Will Learn What is Generative AI? What is ChatGPT and GPT4? What are language models and diffusions models? How do we deploy LangChain and HuggingFace?
ISBN: 9798868802058
Standard No.: 10.1007/979-8-8688-0205-8doiSubjects--Topical Terms:
539563
Application software
--Development.
LC Class. No.: QA76.76.A65
Dewey Class. No.: 005.3
Building generative AI-powered apps = a hands-on guide for developers /
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Generative AI has gone beyond the responsibility of researchers and data scientists and is being used by production engineers. However, there is a lot of confusion where to get started when building an end-to-end app with generative AI. This book consolidates core models, frameworks, and tools into a single source of knowledge. By providing hands-on examples, the book takes you through the generative AI ecosystem to build applications for production. The book starts with a brief and accessible introduction to transformer models before delving into some of the most popular large language models and diffusions models (image generation) These models are the foundations of both AI and your potential new apps. You will then go through various tools available to work with these models, starting with Langchain, a framework to develop foundational models, which is the next building block you should grasp after understanding generative AI models. The next chapters cover databases, caching, monitoring, etc., which are the topics necessary to build larger-scale applications. Real-world examples using these models and tools are included. By the end of this book, you should be able to build end-to-end apps that are powered by generative AI. You also should be able to apply the tools and techniques taught in this book to your use cases and business. What You Will Learn What is Generative AI? What is ChatGPT and GPT4? What are language models and diffusions models? How do we deploy LangChain and HuggingFace?
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based on 0 review(s)
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