
Explore how large language models understand and generate language using transformer architecture, trained on massive text data. Learn prompts, prompt design, and zero-shot and few-shot learning.
Explore LangChain as a framework to build LLM applications by orchestrating OpenAI models, Hugging Face or other providers, and real-time data from Google search and Wikipedia.
Welcome to first LangChain course
This comprehensive course is designed to teach you how to use the LangChain library for building LLM-powered applications.
This course will equip you with the skills and knowledge necessary to develop LLM solutions for a wide range of topics.
The course starts with Introduction to Generative AI, which includes What is Generative AI, What are Discriminative Model and Generative Models and the Training Process of Generative Models.
In the Section 2, Introduction to Large Language Models is presented, which includes What are Large Language Models, Large Language Models Architecture, Difference between Traditional Machine Learning Models and Large Language Models, Use cases of Large Language Models, the lecture also covers, prompt design. Along with this, it also covers Zero Shot Learning and Few Shot Learning.
The Section 3 covers LangChain, What is LangChain, this section also covers OpenAI Large Language Models GPT 3.5, GPT 4, the limitations of these Large Language Models and how does LangChain overcomes these limitations.
The Section 4 covers LangChain, Large Language Models, Prompt Templates, Simple Sequential Chain, Sequential Chain, Agents & Memory.
In the Section 5, a Streamlit Application with LangChain and OpenAI for Instant Book Insights is been build.
The topics covered in this course include:
Generative AI
Large Language Models
LangChain
Prompts, PromptTemplates
Chains: SequentialChain, LLMChain
Agents
Memory
Streamlit (for UI)
Along with lifetime access to the course, you'll get:
Dedicated 1 on 1 troubleshooting support with me
No extra cost for continuous updates and improvements to the course