{"id":24773218,"url":"https://github.com/burnycoder/practical-ai-projects","last_synced_at":"2025-03-23T21:15:09.059Z","repository":{"id":203182561,"uuid":"524857855","full_name":"BurnyCoder/practical-ai-projects","owner":"BurnyCoder","description":"Finetuning LLM, RAG, Multiagents, Image classification\u0026segmentation, Text\u0026Image 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","archived":false,"fork":false,"pushed_at":"2025-03-03T09:26:19.000Z","size":5379,"stargazers_count":1,"open_issues_count":0,"forks_count":0,"subscribers_count":1,"default_branch":"master","last_synced_at":"2025-03-03T10:32:35.422Z","etag":null,"topics":[],"latest_commit_sha":null,"homepage":"","language":"Jupyter Notebook","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":null,"status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/BurnyCoder.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":null,"code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null,"governance":null,"roadmap":null,"authors":null,"dei":null,"publiccode":null,"codemeta":null}},"created_at":"2022-08-15T04:46:19.000Z","updated_at":"2025-03-03T09:26:22.000Z","dependencies_parsed_at":null,"dependency_job_id":"f82469d0-3041-4994-922c-bd4df3ca9951","html_url":"https://github.com/BurnyCoder/practical-ai-projects","commit_stats":null,"previous_names":["burnycoder/ai-image-noise-remover","burnycoder/practical-ai-projects"],"tags_count":0,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/BurnyCoder%2Fpractical-ai-projects","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/BurnyCoder%2Fpractical-ai-projects/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/BurnyCoder%2Fpractical-ai-projects/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/BurnyCoder%2Fpractical-ai-projects/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/BurnyCoder","download_url":"https://codeload.github.com/BurnyCoder/practical-ai-projects/tar.gz/refs/heads/master","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":245168899,"owners_count":20571804,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2022-07-04T15:15:14.044Z","host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":[],"created_at":"2025-01-29T04:39:57.822Z","updated_at":"2025-03-23T21:15:09.043Z","avatar_url":"https://github.com/BurnyCoder.png","language":"Jupyter Notebook","funding_links":[],"categories":[],"sub_categories":[],"readme":"# Practical AI Projects\n\nA comprehensive collection of practical AI applications and implementations using various machine learning, deep learning, and natural language processing techniques.\n\n## Overview\n\nThis 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:\n\n- Retrieval Augmented Generation (RAG)\n- Multi-agent AI systems\n- Language model fine-tuning\n- Computer vision applications\n- Natural language processing\n- Sentiment analysis\n- Recommendation systems\n- Clustering and dimensionality reduction\n\n## Project Structure\n\n### Retrieval Augmented Generation (RAG)\n\n- `retrieval_augmented_generation_agent_llm_llamaindex_gpt-4o.py`: Implements a RAG agent using LlamaIndex and GPT-4o.\n- `graph_retrieval_augmented_generation_graph_rag_gpt-4o.py`: Graph-based RAG implementation using GPT-4o.\n- `retrieval_augmented_generation_query_engine_llamaindex_rag_llm.py`: Query engine for RAG using LlamaIndex.\n\n### Multi-Agent Systems\n\nLocated in the `multi-agent_coding_stock-analysis_customer-onboarding_chess_writing_conversation_autogen` directory:\n\n- `Multi-Agent_Coding_and_Financial_Analysis_AutoGen.ipynb`: Demonstrates collaborative AI agents for coding and financial analysis.\n- `Multi-Agent_Conversation_and_Stand-up_Comedy_AutoGen.ipynb`: AI agents generating conversational content and comedy.\n- `Multi-Agent_Planning_and_Stock_Report_Generation_AutoGen.ipynb`: Agents that plan and generate stock reports.\n- `Multi-Agent_Reflection_and_Blogpost_Writing_AutoGen.ipynb`: Collaborative writing and reflection through AI agents.\n- `Multi-Agent_Sequential_Chats_and_Customer_Onboarding_AutoGen.ipynb`: Customer onboarding flows using multiple agents.\n- `Multi-Agent_Tool_Use_and_Conversational_Chess_AutoGen.ipynb`: Tool use and chess game analysis by AI agents.\n- `Multi-Agent_Tool_Use_Fake_Nvidia_Stocks.py`: Demonstration of tool use for stock analysis.\n\n### Language Model Fine-tuning\n\n- `language_model_finetuning_qlora_llama2.ipynb`: Fine-tuning Llama 2 using QLoRA technique.\n- `finetuning_img_classifier_visual_transformer_lora.ipynb`: Fine-tuning a visual transformer model using LoRA.\n\n### Computer Vision\n\n- `natural-scene-classification_cnns.ipynb`: Classification of natural scenes using CNNs.\n- `image-classifier_resnet18.ipynb`: Image classification using ResNet18.\n- `image-classifier_resnet34.py`: Image classification using ResNet34.\n- `segmentation_resnet34.py`: Image segmentation using ResNet34.\n- `noise_removal_autoencoder.py`: Noise removal using autoencoders.\n- `image_question_answering_openai.py`: Visual question answering using OpenAI's GPT-4o.\n- `text-to-image_clipdrop.py`: Text-to-image generation using ClipDrop.\n\n### Natural Language Processing\n\n- `NLP_classification_search_text-edit_sentiment_analysis_Bert_BoW_SVM_embeddings_skip-gram_regex_POS.ipynb`: Comprehensive NLP techniques.\n- `sentiment-analysis_vader_roberta.ipynb`: Sentiment analysis using VADER and RoBERTa.\n- `sentiment_analysis_AWD-LSTM_enocder.py`: Sentiment analysis using AWD-LSTM encoder.\n- `question_answering_openai.py`: Question answering using OpenAI models.\n- `text-to-speech_elevenlabs.py`: Text-to-speech conversion using ElevenLabs.\n\n### Machine Learning\n\n- `clustering_kmeans.ipynb`: K-means clustering implementation.\n- `dimensionality_reduction_pca.ipynb`: Principal Component Analysis (PCA) for dimensionality reduction.\n- `tabular_prediction_nn.py`: Neural network for tabular data prediction.\n- `digit-classifier_nn.py`: Neural network for digit classification.\n- `recommender_system_movies_nn.py`: Movie recommendation system using neural networks.\n\n## Getting Started\n\n### Prerequisites\n\n- Python 3.8+\n- Required packages depending on the specific project (see individual files)\n- API keys for various services (OpenAI, ElevenLabs, etc.)\n\n### Installation\n\n1. Clone this repository:\n   ```bash\n   git clone https://github.com/yourusername/practical-ai-projects.git\n   cd practical-ai-projects\n   ```\n\n2. Create a virtual environment:\n   ```bash\n   python -m venv venv\n   source venv/bin/activate  # On Windows: venv\\Scripts\\activate\n   ```\n\n3. Install dependencies for the specific project you want to run.\n\n### Usage\n\nEach file or notebook contains example usage. For Python scripts, you can run them directly:\n\n```bash\npython retrieval_augmented_generation_agent_llm_llamaindex_gpt-4o.py\n```\n\nFor Jupyter notebooks, open them in Jupyter Lab or Notebook:\n\n```bash\njupyter lab\n```\n\n## API Key Setup\n\nMany of these projects require API keys. Create a `.env` file in the root directory with the following content:\n\n```\nOPENAI_API_KEY=your_openai_api_key\nELEVENLABS_API_KEY=your_elevenlabs_api_key\n# Add any other API keys required\n```\n\n## License\n\n[MIT License]\n\n## Contributing\n\nContributions are welcome! Please feel free to submit a Pull Request.\n\n## Acknowledgments\n\n- OpenAI for GPT models\n- LlamaIndex for RAG implementations\n- AutoGen for multi-agent systems\n- Various other open-source libraries and frameworks used in the projects\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fburnycoder%2Fpractical-ai-projects","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fburnycoder%2Fpractical-ai-projects","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fburnycoder%2Fpractical-ai-projects/lists"}