Comprehensive Guide to Generative AI Development and Implementation

Comprehensive Guide to Generative AI Development and Implementation

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Introduction to LlamaIndex & end to end Demo

53 of 68

53 of 68

Introduction to LlamaIndex & end to end Demo

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Classroom Contents

Comprehensive Guide to Generative AI Development and Implementation

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  1. 1 Course Introduction
  2. 2 Introduction of the Instructor
  3. 3 Introduction to Generative AI
  4. 4 End to end Generative AI Pipeline
  5. 5 Data Preprocessing & cleaning
  6. 6 Data representation & vectorization for the model training
  7. 7 Text Classification Practical
  8. 8 Introduction to Large Language Models & its architecture
  9. 9 In depth intuition of Transformer-Attention all your need Paper
  10. 10 How ChatGPT is trained
  11. 11 Introduction of Hugging Face
  12. 12 Hands-On Hugging Face - Transformers, HF Pipeline, Datasets, LLMs
  13. 13 Data processing,Tokenizing and Feature Extraction with hugging face
  14. 14 Fine-tuning using a pretrain models
  15. 15 Hugging face API key generation
  16. 16 Project: Text summarization with hugging face
  17. 17 Project: Text to Image generation with LLM with hugging face
  18. 18 Project: Text to speech generation with LLM with hugging face
  19. 19 Introduction to OpenAI
  20. 20 How to generate OpenAI API key?
  21. 21 Local Environment Setup
  22. 22 Hands on OpenAI - ChatCompletion API and Completion API
  23. 23 Function Calling in OpenAI
  24. 24 Project: Telegram bot using OpenAI
  25. 25 Project: Finetuning of GPT-3 model for text classification
  26. 26 Project: Audio Transcript Translation with Whishper
  27. 27 Project: Image genration with DALL-E
  28. 28 Mastering Prompt Engineering
  29. 29 The Complete Introduction to Vector Databases
  30. 30 Mastering Vector Databases with ChromaDB
  31. 31 Mastering Vector Databases with Pinecone
  32. 32 Mastering Vector Databases with Weaviate
  33. 33 Introduction & Installation and setup of langchain
  34. 34 Prompt Templates in Langchain
  35. 35 Chains in Langchain
  36. 36 Langchain Agents and Tools
  37. 37 Memory in Langchain
  38. 38 Documents Loader in Langchain
  39. 39 Multi-Dataframe Agents in Langchain
  40. 40 How to use Hugging face Open Source LLM with Langchain
  41. 41 Project: Interview Questions Creator Application
  42. 42 Project: Custom Website Chatbot
  43. 43 Introduction to Open Source LLMs - Llama
  44. 44 How to use open source llms with Langchain
  45. 45 Custom Website Chatbot using Open source LLMs
  46. 46 Open Source LLMs - Falcon
  47. 47 Introduction & Importance of RAG
  48. 48 RAG Practical demo
  49. 49 RAG Vs Fine-tuning
  50. 50 Build a Q&A App with RAG using Gemini Pro and Langchain
  51. 51 What is Fine Tuning? Parameter Efficient Fine-Tuning - LoRA & QLoRA
  52. 52 Fine-Tuning Meta Llama 2 on Custom Data
  53. 53 Introduction to LlamaIndex & end to end Demo
  54. 54 Open Source Mistral LLM with LlamaIndex
  55. 55 Project: Financial Stock Analysis using LlamaIndex
  56. 56 Project: End to End Medical Chatbot with LLM, Pinecone, LangChain
  57. 57 Project: End to End Source Code Analysis with LangChain, LLM and ChromaDB
  58. 58 Project: Implementing Zomato chatbot with Chainlit
  59. 59 How to Deploy Generative AI Application as CICD on AWS
  60. 60 Introduction to LLMOps & Why we need it?
  61. 61 Generative AI with Google Cloud Vertex AI a LLMOps Platform
  62. 62 Vertex AI Hands-On on Google Cloud
  63. 63 Vertex AI Local Setup - Run Gemini Pro on Local Machine
  64. 64 RAG on Vertex AI with Vector Search and Gemini Pro
  65. 65 LLM powered application on Vertex AI
  66. 66 Fine-tuning Foundation Model on VertexAI
  67. 67 Introduction to AWS Bedrock
  68. 68 End to End RAG Project using AWS Bedrock

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