Quick Start Guide to Large Language Models (LLMs): ChatGPT, Llama, Embeddings, Fine-Tuning, and Multimodal AI (Video Course), 2nd Edition
- By Sinan Ozdemir
- Published Sep 5, 2024 by Pearson IT Certification.
Online Video
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Description
- Copyright 2025
- Edition: 2nd
- Online Video
- ISBN-10: 0-13-538478-8
- ISBN-13: 978-0-13-538478-7
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.
Related Learning:
- Book: Quick Start Guide to Large Language Models, Second edition, https://www.oreilly.com/library/view/quick-start-guide/9780135346570
- Video: Introduction to Transformer Models for NLP, https://learning.oreilly.com/videos/introduction-to-transformer/9780137923717/
- Live Training: https://learning.oreilly.com/search/?q=sinan%20ozdemir&type=live-event- series
- Audio: AI Unveiled, https://learning.oreilly.com/videos/ai-unveiled-audio/9780138364885/
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
Who Should Take This Course:
- Machine learning engineers with experience in ML, neural networks, and NLP
- Developers, data scientists, and engineers who are interested in using LLMs for their projects
- Those who want the best outputs from Generative LLMs and Embedding models
- Those interested in state-of-the-art NLP architecture
- Those interested in productionizing and fine-tuning LLMs
- Those comfortable using libraries like Tensorflow or PyTorch
- Those comfortable with linear algebra and vector/matrix operations
Course Requirements:
- Python 3 proficiency with some experience working in interactive Python environments including Notebooks (Jupyter/Google Colab/Kaggle Kernels)
- Comfortable using the Pandas library and either Tensorflow or PyTorch
- Understanding of ML/deep learning fundamentals including train/test splits, loss/cost functions, and gradient descent
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