Leveraging Llama2 for Advanced AI Solutions

Overview

Gain the skills and knowledge to harness the power of large language models with our comprehensive course on Llama2. Explore the intricacies of LLM architectures, fine-tuning techniques, and retrieval-augmented generation (RAG).

Dive into practical experience with leading tools like Ollama, LangChain, Streamlit, and Hugging Face.

This course is perfect for Software Engineers, Machine Learning Engineers, Data Scientists, and Engineering Managers looking to innovate with AI. Key highlights include building implementations to analyze Meta's original LLama2 paper, providing deep insights and AI-driven Q&A capabilities.

Enhance your model optimization, explore innovative applications, and lead AI projects with confidence.

Prepare to analyze use cases, identify optimal architectures, and design advanced LLM solutions. Expect to deepen your understanding of AI technologies and unlock the potential of Llama2.

Prerequisites for participants include beginner-level Python knowledge and GitHub and Hugging Face accounts for executing hands-on projects.

A setup with at least 8 GB RAM and 3.8 GB of free storage is required, compatible with macOS or Windows.

By course completion, participants will evaluate and design sophisticated LLM solutions, enhancing their competency in AI application development. Transform your approach to AI projects with our expert-led, hands-on course available through Coursera.

Explore related courses in our categories:

Artificial Intelligence Courses, Machine Learning Courses, LangChain Courses, Streamlit Courses, Hugging Face Courses, Retrieval Augmented Generation Courses, and Ollama Courses.

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