https://github.com/burnycoder/practical-ai-projects

Finetuning LLM, RAG, Multiagents, Image classification&segmentation, Text&Image answering, Text2Speech, Movie recommendation, Dimensionality reduction, Llamaindex, Autogen, PyTorch, TensorFlow, Keras, fastai, NumPy, Skicit-learn, Transformers, OpenAI, ElevenLabs, ResNet, LSTM, Autoencoder, U-Net, SVM, CNNs, Transformer, LoRA, GraphRAG, K-means, PCA
https://github.com/burnycoder/practical-ai-projects

Last synced: over 1 year ago
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Finetuning LLM, RAG, Multiagents, Image classification&segmentation, Text&Image answering, Text2Speech, Movie recommendation, Dimensionality reduction, Llamaindex, Autogen, PyTorch, TensorFlow, Keras, fastai, NumPy, Skicit-learn, Transformers, OpenAI, ElevenLabs, ResNet, LSTM, Autoencoder, U-Net, SVM, CNNs, Transformer, LoRA, GraphRAG, K-means, PCA

• Host: GitHub
• URL: https://github.com/burnycoder/practical-ai-projects
• Owner: BurnyCoder
• Created: 2022-08-15T04:46:19.000Z (almost 4 years ago)
• Default Branch: master
• Last Pushed: 2025-03-03T09:26:19.000Z (over 1 year ago)
• Last Synced: 2025-03-03T10:32:35.422Z (over 1 year ago)
• Language: Jupyter Notebook
• Homepage:
• Size: 5.13 MB
• Stars: 1
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

          
# Practical AI Projects

A comprehensive collection of practical AI applications and implementations using various machine learning, deep learning, and natural language processing techniques.

## Overview

This repository contains a diverse set of practical AI projects that demonstrate the application of AI in various domains. The projects cover a wide range of techniques including:

- Retrieval Augmented Generation (RAG)

- Multi-agent AI systems

- Language model fine-tuning

- Computer vision applications

- Natural language processing

- Sentiment analysis

- Recommendation systems

- Clustering and dimensionality reduction

## Project Structure

### Retrieval Augmented Generation (RAG)

- `retrieval_augmented_generation_agent_llm_llamaindex_gpt-4o.py`: Implements a RAG agent using LlamaIndex and GPT-4o.

- `graph_retrieval_augmented_generation_graph_rag_gpt-4o.py`: Graph-based RAG implementation using GPT-4o.

- `retrieval_augmented_generation_query_engine_llamaindex_rag_llm.py`: Query engine for RAG using LlamaIndex.

### Multi-Agent Systems

Located in the `multi-agent_coding_stock-analysis_customer-onboarding_chess_writing_conversation_autogen` directory:

- `Multi-Agent_Coding_and_Financial_Analysis_AutoGen.ipynb`: Demonstrates collaborative AI agents for coding and financial analysis.

- `Multi-Agent_Conversation_and_Stand-up_Comedy_AutoGen.ipynb`: AI agents generating conversational content and comedy.

- `Multi-Agent_Planning_and_Stock_Report_Generation_AutoGen.ipynb`: Agents that plan and generate stock reports.

- `Multi-Agent_Reflection_and_Blogpost_Writing_AutoGen.ipynb`: Collaborative writing and reflection through AI agents.

- `Multi-Agent_Sequential_Chats_and_Customer_Onboarding_AutoGen.ipynb`: Customer onboarding flows using multiple agents.

- `Multi-Agent_Tool_Use_and_Conversational_Chess_AutoGen.ipynb`: Tool use and chess game analysis by AI agents.

- `Multi-Agent_Tool_Use_Fake_Nvidia_Stocks.py`: Demonstration of tool use for stock analysis.

### Language Model Fine-tuning

- `language_model_finetuning_qlora_llama2.ipynb`: Fine-tuning Llama 2 using QLoRA technique.

- `finetuning_img_classifier_visual_transformer_lora.ipynb`: Fine-tuning a visual transformer model using LoRA.

### Computer Vision

- `natural-scene-classification_cnns.ipynb`: Classification of natural scenes using CNNs.

- `image-classifier_resnet18.ipynb`: Image classification using ResNet18.

- `image-classifier_resnet34.py`: Image classification using ResNet34.

- `segmentation_resnet34.py`: Image segmentation using ResNet34.

- `noise_removal_autoencoder.py`: Noise removal using autoencoders.

- `image_question_answering_openai.py`: Visual question answering using OpenAI's GPT-4o.

- `text-to-image_clipdrop.py`: Text-to-image generation using ClipDrop.

### Natural Language Processing

- `NLP_classification_search_text-edit_sentiment_analysis_Bert_BoW_SVM_embeddings_skip-gram_regex_POS.ipynb`: Comprehensive NLP techniques.

- `sentiment-analysis_vader_roberta.ipynb`: Sentiment analysis using VADER and RoBERTa.

- `sentiment_analysis_AWD-LSTM_enocder.py`: Sentiment analysis using AWD-LSTM encoder.

- `question_answering_openai.py`: Question answering using OpenAI models.

- `text-to-speech_elevenlabs.py`: Text-to-speech conversion using ElevenLabs.

### Machine Learning

- `clustering_kmeans.ipynb`: K-means clustering implementation.

- `dimensionality_reduction_pca.ipynb`: Principal Component Analysis (PCA) for dimensionality reduction.

- `tabular_prediction_nn.py`: Neural network for tabular data prediction.

- `digit-classifier_nn.py`: Neural network for digit classification.

- `recommender_system_movies_nn.py`: Movie recommendation system using neural networks.

## Getting Started

### Prerequisites

- Python 3.8+

- Required packages depending on the specific project (see individual files)

- API keys for various services (OpenAI, ElevenLabs, etc.)

### Installation

1. Clone this repository:

   ```bash

   git clone https://github.com/yourusername/practical-ai-projects.git

   cd practical-ai-projects

   ```

2. Create a virtual environment:

   ```bash

   python -m venv venv

   source venv/bin/activate  # On Windows: venv\Scripts\activate

   ```

3. Install dependencies for the specific project you want to run.

### Usage

Each file or notebook contains example usage. For Python scripts, you can run them directly:

```bash

python retrieval_augmented_generation_agent_llm_llamaindex_gpt-4o.py

```

For Jupyter notebooks, open them in Jupyter Lab or Notebook:

```bash

jupyter lab

```

## API Key Setup

Many of these projects require API keys. Create a `.env` file in the root directory with the following content:

```

OPENAI_API_KEY=your_openai_api_key

ELEVENLABS_API_KEY=your_elevenlabs_api_key

# Add any other API keys required

```

## License

[MIT License]

## Contributing

Contributions are welcome! Please feel free to submit a Pull Request.

## Acknowledgments

- OpenAI for GPT models

- LlamaIndex for RAG implementations

- AutoGen for multi-agent systems

- Various other open-source libraries and frameworks used in the projects