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Building LLM Powered  Applications

Building LLM Powered Applications

By : Valentina Alto
4.2 (22)
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Building LLM Powered  Applications

Building LLM Powered Applications

4.2 (22)
By: Valentina Alto

Overview of this book

Building LLM Powered Applications delves into the fundamental concepts, cutting-edge technologies, and practical applications that LLMs offer, ultimately paving the way for the emergence of large foundation models (LFMs) that extend the boundaries of AI capabilities. The book begins with an in-depth introduction to LLMs. We then explore various mainstream architectural frameworks, including both proprietary models (GPT 3.5/4) and open-source models (Falcon LLM), and analyze their unique strengths and differences. Moving ahead, with a focus on the Python-based, lightweight framework called LangChain, we guide you through the process of creating intelligent agents capable of retrieving information from unstructured data and engaging with structured data using LLMs and powerful toolkits. Furthermore, the book ventures into the realm of LFMs, which transcend language modeling to encompass various AI tasks and modalities, such as vision and audio. Whether you are a seasoned AI expert or a newcomer to the field, this book is your roadmap to unlock the full potential of LLMs and forge a new era of intelligent machines.
Table of Contents (16 chapters)
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Index

Existing recommendation systems

Modern recommendation systems uses Machine Learning (ML) techniques to make better predictions about user’s preferences, based on the available data that can come from:

  • User behavior datainsights about user interaction with a product. This data can be acquired from factors like user ratings, clicks, and purchase records.
  • User demographic data refers to personal information about users, including details like age, educational background, income level, and geographical location.
  • Product attribute data involves information about the characteristics of a product, such as the genre for books, cast for movies, or cuisine for food."

As of today, some of the most popular ML rechniques are K-nearest neighbors, dimensionality reduction and Neural networks.

K-Nearest Neighbors

K-nearest neighbor (KNN) is a machine learning algorithm that can be used for both classification and regression problems. It works by finding the k closest data points to a new...

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