Build 19 GEN AI PROJECTS FAST with @LangChain @LlamaIndex @HuggingFace

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Learn how to build 19 GEN AI projects quickly with LangChain, LlamaIndex, and HuggingFace in this hands-on tutorial. Start creating your own AI projects today!


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CHAPTERS:
00:00 - Implementing Text Summarization Project
00:52 - What is Text Summarization
04:00 - Installing Required Libraries
05:58 - Loading Tokenizer and Model
07:42 - Loading Dataset and Data Exploration
26:55 - Stable Diffusion Excel Base 1.0
27:44 - Using Diffusion Model
32:00 - Learning Parameters of Diffusion Model
52:30 - Writing Code to Connect with Telegram
57:01 - Initializing the Bot and Dispatcher
58:16 - What is Async and Await
59:26 - Creating the Start Command Handler
1:02:30 - Creating the Echo Command Handler
1:12:46 - Testing the Bot
1:16:22 - Fine Tuning GPT-3 Model with Custom Data
1:45:05 - Image Generation with DALL-E Model
1:50:42 - Project 2 - Interview Question Creator
1:52:59 - Project Requirements
1:55:20 - Project Architecture
2:15:00 - How Token Text Splitter Works
2:19:45 - Generating Questions from Each Chunk
2:32:00 - Creating Constructor File
2:34:00 - Creating Helper File
2:39:20 - Creating the FastAPI
2:48:24 - Custom Website Chatbot - Intro
2:49:25 - Custom Website Chatbot - Architecture Diagram
3:02:01 - Creating Custom Chatbot
3:05:26 - Website Bot - Project Idea
3:10:08 - Project Architecture
3:18:04 - Installing Required Libraries
3:19:37 - Collecting Website URLs
3:45:08 - Intro
3:46:08 - Financial Stock Analysis Project
3:47:16 - Project Demonstration
3:50:14 - How to Run This Project
3:59:50 - Medical Chatbot - End to End Project
4:01:41 - Medical Chatbot - Project Architecture
4:04:18 - How to Create a Knowledge Base
4:12:50 - Creating the Knowledge Base
4:12:53 - Agenda
4:13:22 - Creating GitHub Repository
4:20:57 - Creating the Folder Structure for the Project
4:24:27 - Creating the Constructor File
4:28:55 - Writing the Python Script to Create the Folder Structure
4:32:28 - Setting Up the Project Environment
4:46:15 - Storing Vector in Pinecone
4:48:43 - Loading Existing Index
4:50:48 - Integrate LLM with Pinecone
4:54:40 - Final Demo
5:12:31 - Creating GitHub Repository
5:13:05 - Setting Up the Project
5:13:38 - Implementing Source Code Analysis
5:15:40 - Project Architecture Diagram
5:21:20 - Importing Required Libraries
5:22:02 - How to Clone Any GitHub Repository Using Python
5:24:41 - How to Extract All the Source Code from the Repository
5:25:56 - How to Perform Context Splitting
5:26:49 - How to Convert Source Code to Embedding and Store in the Knowledge Base
5:29:16 - How to Perform the Entire Operation and Get the Answer from the Model
5:31:26 - Implementing End to End Project
5:32:55 - Creating Template and Static Files
5:33:38 - Implementing app.py
5:48:39 - Creating GitHub Repository
5:49:43 - Environment Setup
5:54:07 - Running ChatGPT in Chalice
5:59:13 - Creating a Custom Chatbot Using LLM
6:18:15 - What is CI/CD
6:18:20 - Tools and Technologies
6:18:31 - AWS Account
6:18:46 - Architecture Overview
6:24:15 - High Level Architecture of Deployment
6:41:26 - Deploying the Application to AWS EC2 Instance
6:47:02 - Implementing RAG with Vertex AI
7:03:20 - Implementing LLM Powered Application
7:06:40 - Setting Up Everything in Local Machine
7:12:40 - How to Use Bedrock API
7:16:40 - Final Words
7:18:35 - Outro

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