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Introduction to Large Language Models (LLMs) and Prompt Engineering torrent


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Torrent language: English English
Total Size: 1.50 GB
Number of files:
52   
Uploader:
freecoursewb
Torrent added:2025-05-29 08:45:44

Download Introduction to Large Language Models (LLMs) and Prompt Engineering torrent




Torrent Description
Introduction to Large Language Models (LLMs) and Prompt Engineering

https://WebToolTip.com

Released 5/2025
By Sinan Ozdemir
MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz, 2 Ch
Genre: eLearning | Language: English + subtitle | Duration: 5h | Size: 1.51 GB

Learn how to use and launch large language models (LLMs) like GPT, Llama, Claude T5, and BERT and design prompts for optimal AI workloads.

Introduction to Large Language Models (LLMs) and Prompt Engineering guides you to launch LLMs like GPT, Llama, Claude, T5, and BERT at scale. It presents a step-by-step approach to building and deploying LLMs, with real-world case studies to illustrate the concepts. It also covers how to begin your LLM journey with prompt engineering with optimal instruction placements and prompting across models. The video works toward building a Retrieval-Augmented Generation (RAG) system with LLMs. It fills a gap in the market by providing a guide to using LLMs and will be a valuable resource for anyone looking to use LLMs in their projects.
The companion GitHub repository for this course is athttps://github.com/sinanuozdemir/quick-start-guide-to-llms

About the Instructor
Sinan Ozdemir is founder and CTO of LoopGenius, where he uses state-of-the-art AI to help people create and run their businesses. He has lectured in data science at Johns Hopkins University and authored multiple books, videos, and numerous online courses on data science, machine learning, and generative AI. He also founded the recently acquired Kylie.ai, an enterprise-grade conversational AI platform with RPA capabilities. Sinan most recently published Quick Guide to Large Language Models, 2nd Edition, and launched a podcast audio series, AI Unveiled. Ozdemir holds a master’s degree in pure mathematics from Johns Hopkins University.

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