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Decoding Large Language Models

Decoding Large Language Models

By : Irena Cronin
4 (3)
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Decoding Large Language Models

Decoding Large Language Models

4 (3)
By: Irena Cronin

Overview of this book

Ever wondered how large language models (LLMs) work and how they're shaping the future of artificial intelligence? Written by a renowned author and AI, AR, and data expert, Decoding Large Language Models is a combination of deep technical insights and practical use cases that not only demystifies complex AI concepts, but also guides you through the implementation and optimization of LLMs for real-world applications. You’ll learn about the structure of LLMs, how they're developed, and how to utilize them in various ways. The chapters will help you explore strategies for improving these models and testing them to ensure effective deployment. Packed with real-life examples, this book covers ethical considerations, offering a balanced perspective on their societal impact. You’ll be able to leverage and fine-tune LLMs for optimal performance with the help of detailed explanations. You’ll also master techniques for training, deploying, and scaling models to be able to overcome complex data challenges with confidence and precision. This book will prepare you for future challenges in the ever-evolving fields of AI and NLP. By the end of this book, you’ll have gained a solid understanding of the architecture, development, applications, and ethical use of LLMs and be up to date with emerging trends, such as GPT-5.
Table of Contents (22 chapters)
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1
Part 1: The Foundations of Large Language Models (LLMs)
4
Part 2: Mastering LLM Development
9
Part 3: Deployment and Enhancing LLM Performance
14
Part 4: Issues, Practical Insights, and Preparing for the Future

Summary

Language models such as GPT-4 are built on a foundation of complex neural network architectures and processes, each serving critical roles in understanding and generating text. These models start with extensive training data encompassing a diverse array of topics and writing styles, which is then processed through tokenization to convert text into a numerical format that neural networks can work with. GPT-4, specifically, employs the Transformer architecture, which eliminates the need for sequential data processing inherent to RNNs and leverages self-attention mechanisms to weigh the importance of different parts of the input data. Embeddings play a crucial role in this architecture by converting words or tokens into vectors that capture semantic meaning and incorporate the order of words through positional embeddings.

User interaction significantly influences the performance and output quality of models such as GPT-4. Through prompts, feedback, and corrections, users shape...

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