Pearson – Quick Start Guide to Large Language Models (LLMs) ChatGPT, Llama, Embeddings
English | Tutorial | Size: 4.3 GB
13+ Hours of Video Instruction
Learn how to use and launch large language models (LLMs) like GPT, Llama, T5, and BERT at scale through real-world case studies.
Quick Guide to ChatGPT, Embeddings, and Other Large Language Models (LLMs) Second Edition is a quick start guide to help people use and launch LLMs like GPT, Llama, 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. The video covers topics such as constructing agents, fine-tuning a Llama 3 model with RLHF, building recommendation engines with Siamese BERT architectures, launching an information retrieval system with OpenAI embeddings and GPT-4, and building an image captioning system with the vision transformer and GPT. This guide provides clear instructions and best practices for using 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.
Large language models (LLMs) are a type of artificial intelligence (AI) that use deep learning to process natural language. LLMs are trained on large datasets of text and can be used to generate text, answer questions, and perform other tasks related to natural language processing. LLMs are becoming increasingly popular for a variety of applications, such as recommendation engines, information retrieval systems, image captioning, and translation/summarization pipelines. LLMs are also being used to build chatbots to have conversations that change their style of speaking depending on who they are talking to. LLMs are powerful tools that can help organizations and individuals make sense of large amounts of data and generate insights that would otherwise be difficult to obtain.
Skill Level:
Intermediate
Advanced
Learn How To:
Apply large language models (LLMs) and use semantic search with them
Utilize principles of prompt engineering to build agents and a retrieval-augmented generation (RAG) bot with OpenAI and GPT-4
Optimize fine-tuning LLMs for speed and performance
Use advanced prompt engineering principles
Customize embeddings architectures
Engage in AI alignment
Use advanced models and fine-tuning
Move quantized LLMs into production
Evaluate LLMs for both generative and understanding tasks
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