https://github.com/iamkankan/generative-ai-tutorials

Generative AI Full Course
https://github.com/iamkankan/generative-ai-tutorials

gemini-pro langchain llama more openapi pinecone vector-database

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Generative AI Full Course

• Host: GitHub
• URL: https://github.com/iamkankan/generative-ai-tutorials
• Owner: iAmKankan
• Created: 2024-06-22T20:39:39.000Z (5 months ago)
• Default Branch: main
• Last Pushed: 2024-07-07T17:39:59.000Z (4 months ago)
• Last Synced: 2024-10-29T08:36:32.157Z (18 days ago)
• Topics: gemini-pro, langchain, llama, more, openapi, pinecone, vector-database
• Homepage:
• Size: 77.1 KB
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

        
## Generative-AI-tutorials

![](https://user-images.githubusercontent.com/12748752/150695343-8977b5d0-3cd4-4959-b90e-9fe72d336d42.png)

### $\Large{\color{Purple}\textbf{Agenda:}}$

* Generative AI?

* Large Language Models LLMs?

* OpenAI

* Langchain

* VectorDatabase

* Llama Index

* Open Source LLM model

* End to End project

  

### Neural Network



 



### What is Generative AI?

$\Large Answer:$ Generative AI generates new data based on the training samples. Generative model can generate Image, textt, Audio, Video .etc. data as output.

Generative AI has mainly two parts

1) Generative Language Models. example Large Language Models(LLMS)

2) Generative Image Models.

### Generative Al



  



#### Example of Large language models

* ChatGPT

* Google Bard

* Meta Llama2

### What is Generative Al?

Generative Al generate new data, based on training sample.Generative model can generate Image, Text, Audio, Videos etc. data as output.

So generative Al is a very huge topics,

* Generative Image model

* Generative Language model

### GAN Structure 

A **generative adversarial network (GAN)** has two parts:

* The generator learns to generate plausible data. The generated instances become negative training examples for the discriminator.

* The discriminator learns to distinguish the generator's fake data from real data. The discriminator penalizes the generator for producing implausible results.