NeurAI Explorer

NeurAI Explorer is an advanced Python-based tool designed to dynamically adjust neural network architectures and hyperparameters based on the latest findings in machine learning research. The tool scrapes recent research papers from arXiv, processes them using natural language processing (NLP), and incorporates new findings into neural network models automatically.

Table of Contents

• Features
• Requirements
• Installation
• Usage
• Project Structure
• Contributing

Features

• Web Scraping: Automatically scrapes the latest machine learning papers from arXiv.
• NLP-Based Analysis: Analyzes the content of the papers using state-of-the-art NLP models.
• Dynamic Model Building: Adjusts neural network architectures and hyperparameters based on the extracted insights.
• Automation: Supports task automation through Celery, enabling scheduled updates of models.
• Customizable: Easily configurable for different machine learning subdomains (e.g., computer vision, NLP).

Requirements

Before setting up the project, ensure that the following dependencies are installed:

• Python 3.8+
• arxiv for fetching papers
• requests for HTTP requests
• PyPDF2 or pdfminer.six for extracting text from PDFs
• spaCy for NLP
• transformers for advanced text analysis
• PyTorch or TensorFlow for neural network modeling
• Celery for task automation

Install these dependencies using the following command:

pip install -r requirements.txt

Installation

1. Clone the repository:

   git clone https://github.com/yourusername/neurai-explorer.git
   cd neurai-explorer

2. Set up a virtual environment (optional but recommended):

   python -m venv venv
   source venv/bin/activate  # On Windows: venv\Scripts\activate

3. Install the required dependencies:

   pip install -r requirements.txt

4. Download the required spaCy language model:

   python -m spacy download en_core_web_sm

Usage

1. Fetch recent research papers from arXiv:

Run the script to fetch the latest research papers:

python main.py

2. Automate the process with Celery:

Set up Celery to automate fetching, processing, and adapting models:

1. Start the Celery worker:

   celery -A tasks worker --loglevel=info

2. (Optional) Schedule periodic updates with Celery Beat:

   You can configure tasks.py to run updates at regular intervals.

3. Customize Neural Network Model Building:

To customize how neural networks are built based on paper content, modify the logic in model_adaptation/build_model.py. The model architecture adapts based on keywords extracted from the research papers.

4. Configure Categories and Parameters:

You can modify the research category (e.g., computer vision, NLP) in the data_acquisition/fetch_papers.py file by adjusting the category parameter.

Project Structure

neurAI_explorer/
├── data_acquisition/
│   ├── __init__.py
│   └── fetch_papers.py       # Fetches recent papers from arXiv
├── data_processing/
│   ├── __init__.py
│   └── process_papers.py     # Extracts and preprocesses paper content
├── nlp_analysis/
│   ├── __init__.py
│   └── analyze_papers.py     # Analyzes paper content using NLP
├── model_adaptation/
│   ├── __init__.py
│   └── build_model.py        # Dynamically builds neural networks
├── integration/
│   ├── __init__.py
│   └── tasks.py              # Celery tasks for automation
├── ui/
│   ├── app.py                # Flask/Streamlit app (optional)
│   └── templates/            # HTML templates for the UI (optional)
├── requirements.txt          # Project dependencies
└── README.md                 # Project documentation

Contributing

I welcome contributions! Here's how you can help:

1. Fork the repository.
2. Create a new branch: git checkout -b feature-branch.
3. Commit your changes: git commit -m "Add some feature".
4. Push to the branch: git push origin feature-branch.
5. Submit a pull request.

email me at yzjandali@gmail.com if you have an questions!