Comprehensive Guide to Generative AI Projects - From Basics to Advanced Applications

Comprehensive Guide to Generative AI Projects - From Basics to Advanced Applications

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Introduction to Google AI Studio

6 of 61

6 of 61

Introduction to Google AI Studio

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Comprehensive Guide to Generative AI Projects - From Basics to Advanced Applications

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  1. 1 Prompt Engineering for ChatGPT
  2. 2 Introduction to Google Gemini 1.5 Pro
  3. 3 Introduction to Google Gemini
  4. 4 Agenda of Google Gemini 1.5 Pro
  5. 5 Versions of Google Gemini
  6. 6 Introduction to Google AI Studio
  7. 7 Gemini Models in Google AI Studio
  8. 8 Tokens in Google AI Studio
  9. 9 Advance Settings in Google AI Studio
  10. 10 Temperature in Google AI Studio
  11. 11 Google AI Studio Left Panel Features
  12. 12 Vertex AI on Google Cloud
  13. 13 General Prompt Demo Google AI Studio
  14. 14 Structured Prompt in Google AI Studio
  15. 15 Model Tuning in Google AI Studio using System Sample
  16. 16 Analysing video Google AI Studio using System Sample
  17. 17 Google Gemini 1.5 Pro Vs Flash
  18. 18 Python Get and Post Method Demo
  19. 19 Python and Google Gemini 1.5 Pro Integration
  20. 20 Get API Key
  21. 21 What is Chatbot?
  22. 22 Types of Chatbots
  23. 23 Applications of Chatbots
  24. 24 Building Chatbot -Theory
  25. 25 Demo on Building Chatbot
  26. 26 Google Gemini 1.5 Pro Vs ChatGPT 4-o versions
  27. 27 Promot Based Trails
  28. 28 Prompt Trail on Problem-Solving and Technical Assistance
  29. 29 Generative AI
  30. 30 Overview of Python and Introduction to GenAI
  31. 31 Development Environment Setup
  32. 32 Generative AI Full course 2024 | All in One Gen AI Tutorial
  33. 33 Introduction to OpenAPI GPT API
  34. 34 Flask ChatGPT App
  35. 35 LangChain Apps
  36. 36 Introduction to LangChain
  37. 37 Agenda of LangChain
  38. 38 How Generative AI will transform our lives in 2023 | Automation with AI tools | ChatGPT | AI Tools
  39. 39 Why LangChain?
  40. 40 Development Environment Setup of LangChain
  41. 41 Demo on Library Installation
  42. 42 LangChain Core Concepts
  43. 43 LangChain Components
  44. 44 LangChain Case Study
  45. 45 Add LangChain App
  46. 46 LangChain App Execution
  47. 47 Introduction to LLMs
  48. 48 Limitations of LLMs
  49. 49 Introduction to RAG
  50. 50 RAG Basics Concepts and Terminology
  51. 51 Key Components - Retrieval and Generation
  52. 52 Workflow of RAG
  53. 53 Steps to implement RAG with LangChain
  54. 54 Hands-on RAG - Importing Libraries
  55. 55 Hands-on RAG in detail
  56. 56 Why are LLMs Important
  57. 57 Abstract of the Email Generator App
  58. 58 Generative AI crash course
  59. 59 Software Requirements for App
  60. 60 Implementation of the App
  61. 61 Executing the App

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