Leveraging Llama2 for Advanced AI Solutions
This course equips learners to design, develop, and optimize advanced LLM solutions using LLama2. It covers LLM architectures, fine-tuning, RAG, and tools like LangChain and Hugging Face, offering hands-on experience with cutting-edge AI techniques.
Overview
This course includes:
- 2 hours of on-demand video
- Certificate of completion
- Direct access/chat with the instructor
- 100% self-paced online
The focus of this course is to equip learners with the skills and knowledge to design, develop, and optimize advanced large language model (LLM) solutions using LLama2. Topics covered will include a comprehensive understanding of LLM architectures, techniques for fine-tuning LLMs, retrieval-augmented generation (RAG), and the utilization of tools like Ollama, LangChain, Streamlit, and Hugging Face. This course will be exciting for learners as it delves into cutting-edge advancements in AI, offering hands-on experience with state-of-the-art tools and techniques.
Skills You Will Gain
Learning Outcomes (At The End Of This Program, You Will Be Able To...)
- Evaluate LLMs conceptually and comprehend the solution development process.
- Analyze use cases for LLMs and determine Optimal Architectures, Models, and Optimization Techniques.
- Apply and compare Diverse Optimization Techniques for LLM Models.
- Design and develop Advanced LLM Solutions Utilizing LLama2.
Prerequisites
Learners should have basic Python skills, GitHub and Hugging Face accounts, and a system with at least 8GB RAM, 3.8GB free storage, running macOS or Windows.
Who Should Attend
Software Engineers, Machine Learning Engineer, Data Scientist, Engineering Managers
