Neuron_Code Repository

A comprehensive collection of neural networks, machine learning models, and programming tutorials focusing on scientific and engineering applications, particularly in material science.

Repository Overview

This repository contains implementations, tutorials, and examples spanning various domains:

• Machine learning algorithms and data analysis
• Neural network architectures (RNN, CNN)
• Scientific computing applications
• Programming fundamentals in Python
• Database integrations using various technologies
• Docker configurations for database environments

Directory Structure

CS229

Stanford's Machine Learning course implementations:

• 01_Linear_Regression.py: Basic linear regression implementation
• 02_Zomato_Data_Analysis.py: Data analysis of restaurant data
• 03_Wine_Quality_Prediction.py: ML model for wine quality prediction
• Supporting datasets: Wine-Quality.csv, Zomato-data-.csv

MIT_6.S191

MIT's Deep Learning course implementations:

• 01_Learn_Gradient_Descent_Using_Linear_Regression.py: Custom gradient descent implementation
• 02_Recurrent_Neural_Network.py & 03_Recurrent_Neural_Network.py: RNN implementations
• 04_Sin_Wave_Generator_Using_RNN.py: Generating sine waves using RNNs
• 05_Convolution_Neural_Network.ipynb: CNN implementation

MIST_Conference

Material science research for conference:

• Algae_Biofuel_Data_for_MIST_conf.py: Analysis of algae biofuel data
• Algae_Biofuel_Data_for MIST conf.csv: Dataset for biofuel analysis

Tutorials

Programming tutorials organized by topic:

• 01_Basics: Fundamental programming concepts
• 02_Basics_OOP: Object-oriented programming principles
• 03_Decorators: Python decorator patterns and usage
• 04_Error_Handling: Exception management techniques
• 05_Database_Sqlite3: SQLite database integration
• 06_Handling_APIs: API interaction examples
• 07_Database_MongoDB: MongoDB integration

docker-databases

Docker configurations for database environments:

• mongodb: MongoDB container configuration
• mysql: MySQL container setup
• postgresql: PostgreSQL configuration
• redis: Redis server and client setup

Technologies Used

• Programming Languages: Python
• Machine Learning: scikit-learn, NumPy, Pandas
• Deep Learning: PyTorch, TensorFlow (via implementations)
• Data Visualization: Matplotlib, Seaborn
• Notebooks: Marimo (Jupyter alternative)
• Databases: SQLite, MongoDB, MySQL, PostgreSQL, Redis
• Containerization: Docker

Usage

Setting Up the Environment

1. Install the required packages:

   pip install -r requirements.txt
   

2. For database examples, use the Docker configurations:

   cd docker-databases/<database-name>
   docker-compose up
   

Running the Examples

• Python Scripts: Execute with Python interpreter

  python <script_name>.py
  

• Marimo Notebooks: Run with marimo

  marimo run <notebook_name>.py
  

Working with Specific Modules

• CS229 (Machine Learning): Explore statistical models and data analysis techniques
• MIT_6.S191 (Deep Learning): Learn neural network architectures and implementations
• MIST_Conference: Review material science analyses and research code
• Tutorials: Step through various programming concepts from basic to advanced

License

This repository contains educational materials and implementations for learning purposes.