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Building LLM Powered Applications

You're reading from Building LLM Powered Applications Create intelligent apps and agents with large language models

Product type Paperback

Published in May 2024

Publisher Packt

ISBN-13 9781835462317

Length 342 pages

Edition 1st Edition

Languages

Python

Tools

Falcon

Concepts

Artificial Intelligence

Author (1):

Valentina Alto

View More author details

Table of Contents (16) Chapters

Preface

1. Introduction to Large Language Models FREE CHAPTER

2. LLMs for AI-Powered Applications

3. Choosing an LLM for Your Application

4. Prompt Engineering

5. Embedding LLMs within Your Applications

6. Building Conversational Applications

7. Search and Recommendation Engines with LLMs

8. Using LLMs with Structured Data

9. Working with Code

10. Building Multimodal Applications with LLMs

11. Fine-Tuning Large Language Models

12. Responsible AI

13. Emerging Trends and Innovations

14. Other Books You May Enjoy

15. Index

References

Training dataset: https://huggingface.co/datasets/imdb
HF AutoTrain: https://huggingface.co/docs/autotrain/index
BERT paper: Jacob Devlin, Ming-Wei Chang, Kenton Lee, Kristina Toutanova, 2019, BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding: https://arxiv.org/abs/1810.04805

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Authors (1)

Valentina Alto

Valentina Alto

Valentina Alto is a Data Science Graduate who joined Microsoft Italy in 2020 as an Azure solution specialist. Since 2022, she has been focusing on data and AI workloads within the manufacturing and pharmaceutical industries. She has been working closely with system integrators on customer projects to deploy cloud architecture with a focus on Modern Data Platforms and AI-powered applications. In June 2024, she moved to Microsoft Dubai as an AI App Tech Architect to focus more on AI-driven projects in the Middle East. Since commencing her academic journey, she has been writing tech articles on statistics, machine learning, deep learning, and AI in various publications. She has authored several books on machine learning and large language models.

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