Trang chủ

Khoá học

Hands-on Generative AI Engineering with Large Language Model

Implementing Transformer from Scratch, Training, Fine-tuning | LLM-based Assistants, Chatbots, RAG, Agents | Serving

50.000 VND

45 VND

Đầy Đủ Bài Giảng

Học Online Tiện Lợi

Kích Hoạt Nhanh 2-5 Phút

Thanh toán tự động

Được phép tải xuống

Mô tả

Dive into the rapidly evolving world of Generative AI with our comprehensive course, designed for learners eager to build, train, and deploy Large Language Models (LLMs) from scratch.

This course equips you with a wide range of tools, frameworks, and techniques to create your GenAI applications using Large Language Models, including Python, PyTorch, LangChain, LlamaIndex, Hugging Face, FAISS, Chroma, Tavily, Streamlit, Gradio, FastAPI, Docker, and more.

This hands-on course covers essential topics such as implementing Transformers, fine-tuning models, prompt engineering, vector embeddings, vector stores, and creating cutting-edge AI applications like AI Assistants, Chatbots, Retrieval-Augmented Generation (RAG) systems, autonomous agents, and deploying your GenAI applications from scratch using REST APIs and Docker containerization.

By the end of this course, you will have the practical skills and theoretical knowledge needed to engineer and deploy your own LLM-based applications.

Let's look at our table of contents:

Introduction to the Course

Course Objectives
Course Structure
Learning Paths

Part 1: Software Prerequisites for Python Projects

IDE
- VS Code
- PyCharm
Terminal
- Windows: PowerShell, etc.
- macOS: iTerm2, etc.
- Linux: Bash, etc.
Python Installation
- Python installer
- Anaconda distribution
Python Environment
- venv
- conda
Python Package Installation
- PyPI, pip
- Anaconda, conda
Software Used in This Course

Part 2: Introduction to Transformers

Introduction to NLP Before and After the Transformer’s Arrival
Mastering Transformers Block by Block
Transformer Training Process
Transformer Inference Process

Part 3: Implementing Transformers from Scratch with PyTorch

Introduction to the Training Process Implementation
Implementing a Transformer as a Python Package
Calling the Training and Inference Processes
Experimenting with Notebooks

Part 4: Generative AI with the Hugging Face Ecosystem

Introduction to Hugging Face
Hugging Face Hubs
- Models
- Datasets
- Spaces
Hugging Face Libraries
- Transformers
- Datasets
- Evaluate, etc.
Practical Guides with Hugging Face
- Fine-Tuning a Pre-trained Language Model with Hugging Face
- End-to-End Fine-Tuning Example
- Sharing Your Model

Part 5: Components to Build LLM-Based Web Applications

Backend Components
- LLM Orchestration Frameworks: LangChain, LlamaIndex
- Open-Source vs. Proprietary LLMs
- Vector Embedding
- Vector Database
- Prompt Engineering
Frontend Components
- Python-Based Frontend Frameworks: Streamlit, Gradio

Part 6: Building LLM-Based Web Applications

Task-Specific AI Assistants
- Culinary AI Assistant
- Marketing AI Assistant
- Customer AI Assistant
- SQL-Querying AI Assistant
- Travel AI Assistant
- Summarization AI Assistant
- Interview AI Assistant
Simple AI Chatbot
RAG (Retrieval-Augmented Generation) Based AI Chatbot
- Chat with PDF, DOCX, CSV, TXT, Webpage
Agent-Based AI Chatbot
- AI Chatbot with Math Problems
- AI Chatbot with Search Problems

Part 7: Serving LLM-Based Web Applications

Creating the Frontend and Backend as Two Separate Services
Communicating Between Frontend and Backend Using a REST API
Serving the Application with Docker
- Install, Run, and Enable Communication Between Frontend and Backend in a Single Docker Container
Use Case
- An LLM-Based Song Recommendation App

Conclusions and Next Steps

What We Have Learned
Next Steps

Thank You

Bạn sẽ học được gì

Understanding how to build, implement, train, and perform inference on a Large Language Model, such as Transformer (Attention Is All You Need) from scratch.

Gaining knowledge of the different components, tools, and frameworks required to build an LLM-based application, such as LangChain, LlamaIndex, Hugging Face

Learn how to fine-tune a Large Language Model on your custom dataset for specific downstream Natural Language Processing (NLP) tasks

Implementing best practices in prompt engineering to optimize the performance of Large Language Models

Building diverse LLM-based applications, including AI assistants, chatbots, Retrieval-Augmented Generation (RAG) systems, and intelligent agents

Learning how to serve your LLM-based application from scratch with REST API and Docker.

Engaging in hands-on technical implementations: Notebook, Python scripts, building model as a Python package, train, fine-tune, inference and more.

Receiving guidance on advanced engineering topics in Generative AI with Large Language Models.

Yêu cầu

No prior experience in Generative AI, Large Language Models, Natural Language Processing, or Python is needed. This course will provide you with everything you need to enter this field with enthusiasm and curiosity. Concepts and components are first explained theoretically and through documentation, followed by hands-on technical implementations. All code snippets are explained step-by-step, with accompanying Notebook playgrounds and complete Python source code, structured to ensure a clear and comprehensive understanding.

Nội dung khoá học

10 sections

Introduction to the Course

6 lectures

Welcome to the course