Quick Guide to ChatGPT, Embeddings, and Other Large Language Models (LLMs)

9+ Hours of Video Instruction

Learn how to use and launch large language models (LLMs) like GPT, T5, and BERT at scale with real-world case studies.

Overview:
Quick Guide to ChatGPT, Embeddings, and Other Large Language Models (LLMs) is a quick-start guide to help people use and launch LLMs like GPT, T5, and BERT at scale. It shows 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 building recommendation engines with siamese BERT architectures, launching an information retrieval system with OpenAI embeddings and GPT3, and building an image captioning system with the vision transformer and GPT-J. 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,
• Video: Introduction to Transformer Models for NLP,
• Live Training,
• Audio: AI Unveiled,

Companion files for this course are available at https://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, and launched a podcast audio series, AI Unveiled. Ozdemir holds a master's degree in pure mathematics from Johns Hopkins University.

Skill Level:

• Intermediate
• Advanced

Learn How To:

• Launch an application using proprietary models with an example of an information retrieval system with OpenAI embeddings and GPT3 for Question/Answering.
• Fine-tune GPT3 with custom examples using their API to get better results.
• Learn the basics of prompt engineering with GPT3 to get more nuanced examples by building a chatbot with persona style depending on who they are talking to using the information retrieval system.
• Deploy custom LLMs to the cloud

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 the GPT-3 or ChatGPT model
• 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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