Building LLM Applications with Python: A Practical Guide

Anand Vemula · AI-narrated by Madison (from Google)
Audiobook
14 min
Unabridged
AI-narrated
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About this audiobook

This book equips you to harness the remarkable capabilities of Large Language Models (LLMs) using Python.

Part I unveils the world of LLMs. You'll delve into their inner workings, explore different LLM types, and discover their exciting applications in various fields.

Part II dives into the practical side of things. We'll guide you through setting up your Python environment and interacting with LLMs. Learn to craft effective prompts to get the most out of LLMs and understand the different response formats they can generate.

Part III gets you building! We'll explore how to leverage LLMs for creative text generation, from poems and scripts to code snippets. Craft effective question-answering systems and build engaging chatbots – the possibilities are endless!

Part IV empowers you to maintain and improve your LLM creations. We'll delve into debugging techniques to identify and resolve issues. Learn to track performance and implement optimizations to ensure your LLM applications run smoothly.

This book doesn't shy away from the bigger picture. The final chapter explores the ethical considerations of LLMs, addressing bias and promoting responsible use of this powerful technology.

By the end of this journey, you'll be equipped to unlock the potential of LLMs with Python and contribute to a future brimming with exciting possibilities.

About the author

I am Anand V, a seasoned Enterprise Architect with extensive experience in AI and Generative AI technologies. My expertise includes implementing advanced AI solutions such as H20, Google TensorFlow, and MNIST, and leading digital transformation projects incorporating AI/ML, AR/VR, and RPA. I have integrated Generative AI tools, such as OpenAI's GPT, into enterprise architectures to enhance customer experiences and drive innovation. My work includes developing transformer models, fine-tuning pre-trained language models, and implementing neural network architectures for natural language processing (NLP) tasks. Additionally, I have utilized techniques such as deep reinforcement learning, variational autoencoders, and GANs for complex data synthesis and predictive analytics. My leadership in deploying AI-driven methodologies has significantly improved business performance across various industries.

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