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

Quick Guide to ChatGPT, Embeddings, and Other Large Language Models (LLMs) [Update 08/2024]
English | Size: 2.86 GB
Genre: eLearning

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.

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

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