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Akash_AI_ML_Notes

Project Structure

This repository is structured to help you navigate and utilize the notes effectively. Below is the recommended structure:

Akash_AI_ML_Notes/
├── 1_SQL/                            # Learn about basic SQL Queries
├── 2_DATA SCIENCE LIBRARIES          # Learn about the libraries required for Data Science
├── 3_Maths_for_DS_AIML               # Learn about basic Maths and statistics for DS and AIML
├── 4_Machine Learning(ML)            # Learn about ML algorithsm & training ML models
└── README.md                         # Project overview and instructions

About uv

The uv package manager is a Python ecosystem tool similar to npm in the JavaScript ecosystem. It allows you to manage dependencies, scripts, and configurations for your projects efficiently. If you're familiar with npm, you'll find uv intuitive and powerful for managing AI/ML or Python based projects.

Steps to setup your system environment to use these notes optimally:

  • Install Python from the official docs: Docs

  • Install uv using pip:

    pip install uv  # For windows
    
    pip3 install uv # For Mac/Linux
  • Install the requirements using uv from the requirements.txt:

    uv add -r requirements.txt
    
    # Or let uv install from the pyproject.toml
    uv pip install .
    
  • If you need to add any dependency for your learning locally on your system, use these commands:

    uv add <pkg_name> # Install package using uv
    uv pip freeze > requirements.txt  # Freeze your dependencies to used on any system
  • In VS code in .ipynb files we need to select the kernel from the .venv

  • If you want to use jupyter notebooks in your browser view not in vs code, use these commands in the root directory of this project:

    .\.venv\Scripts\Activate.ps1      # For Windows
    
    source .venv/bin/activate         # For Unix based OS (Mac/Linux): 
    
  • These will activate the virtual environment for you and we can use jupyter notebook or jupyter lab to work in the default browser view


Note: For Deep Learning we are using CUDA - 12.6 and cuDNN - 8.9.7.x for developing AI models with PyTorch and Tensorflow.


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This repo contains my AI-ML notes

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