모두를 위한 대규모 언어 모델 LLM(Large Language Model) Part 1 - Llama 2 Fine-Tuning 해보기
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LLM(Large Language Model)의 기초 개념부터 고성능 LLM인 Llama 2 모델을 내가 원하는 데이터셋에 Fine-Tuning하는 방법까지 차근차근 학습합니다.
중급이상
LLM, Llama, 딥러닝
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Large Language Models (LLM) for Everyone Part 2 - Building Your Own ChatGPT with LangChain
This is a course to learn the concepts and usage methods of the LangChain library, and to build your own ChatGPT using the LangChain library.
Reviews from Early Learners
What you will gain after the course
LangChain Library Basics and Usage
Retrieval-Augmented Generation (RAG) Concept
Various Use Cases of Retrieval-Augmented Generation (RAG) Implementation
How to Build My Own ChatGPT with Retrieval-Augmented Generation (RAG)
LangChain for easy LLM implementation,
From concept to practice, all in one place!
Let's implement your own ChatGPT with just a few lines of code using LangChain!
By properly utilizing the LangChain library and the OpenAI API, you can implement your own ChatGPT using the latest LLM model in just a few lines of code.
- ✅ You can learn step-by-step, from the basic concepts of the LangChain library to various use cases of Retrieval-Augmented Generation (RAG) implementation.
- ✅ Create your own ChatGPT using the LangChain library!
Who is this course for?
Those who want to learn the concepts and usage of the LangChain library in a solid manner.
Anyone who wants to create their own ChatGPT using Langchain
Anyone who wants to learn about the various use cases of Retrieval-Augmented Generation (RAG)
Anyone who wants to develop a service using the latest LLM model
Lecture Content 📖
👨💻 We will practice creating various ChatGPTs of our own using LangChain and various datasets.
| We will create JudgeGPT, which allows you to search for case law and check the content of case law using various legal case law data. |
| Let's create PatentGPT, which allows you to search for patents and check patent information using various patent data. |
| We will create a review sentiment analysis GPT (SentimentGPT) that can analyze positive and negative sentiments in reviews using various review data. |
| We will create a Product Recommendation GPT (RecommendationGPT) that recommends products with good ratings and that meet the user's needs using various product review data. |
Player Course ✅
👋 This course requires prior knowledge of Python, Natural Language Processing (NLP), and LLM . Be sure to take the courses below first, or have equivalent knowledge before taking this course.
Q&A 💬
Q. What is LangChain?
The LangChain library is a Python library that provides various functions related to natural language processing (NLP) . Its primary purpose is to provide useful tools for building and researching conversational AI systems . Its features include:
1. Chatbot Building : LangChain provides tools for building chatbots and conversational AI systems. This allows users to easily create their own chatbots .
2. Various NLP functions : This library includes various natural language processing functions such as text generation, summarization, and translation.
3. Plug-and-play architecture : Users can easily integrate LangChain with existing NLP models or systems. This allows for the easy combination of various language models and functions.
4. Scalability and Customization : LangChain is designed to allow users to customize and extend the system to suit their needs. This is a valuable feature for researchers and developers.
5. Research and Development Support : LangChain helps researchers and developers experiment with and develop new conversational AI models.
This library is a valuable tool for developers, researchers, and students interested in research and development related to conversational AI . LangChain allows users to more easily build and experiment with complex NLP systems .
Q. Is player knowledge required?
This lecture [Large Language Model for Everyone LLM (Large Language Model) Part 2 - Building Your Own ChatGPT with LangChain] covers how to build your own ChatGPT using the LangChain library and LLM . Therefore, the lecture proceeds under the assumption that you have basic knowledge of Python, natural language processing, and LLM. If you lack basic knowledge of natural language processing and LLM, we recommend taking the preceding lecture [Large Language Model for Everyone LLM (Large Language Model) Part 1 - Fine-Tuning Llama 2] first.
Recommended for
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Who is this course right for?
Learners of LangChain library concepts and usage.
Those who want to build their own ChatGPT
Deep learning research job seekers
Individuals interested in AI/Deep Learning research
Those preparing for AI graduate school
Need to know before starting?
Python experience
Pre-course: [LLM for Everyone (Large Language Model) Part 1 - Llama 2 Fine-Tuning] Experience
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Curriculum
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46 lectures ∙ (8hr 59min)
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Lecture resources
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