Skip to content

Notes, reviews, and detailed learning paths for deeplearning.ai courses

Notifications You must be signed in to change notification settings

sshkhr/dlai-companion

Folders and files

NameName
Last commit message
Last commit date

Latest commit

 

History

23 Commits
 
 
 
 
 
 
 
 

Repository files navigation

DeepLearning.AI Companion

Built with Material for MkDocs GitHub Stars Contributions Welcome MIT License GitHub Pages

Welcome to the DeepLearning.AI Companion project! This repository contains comprehensive notes and reviews for various courses offered by DeepLearning.AI. The notes are written in Markdown and are compiled into a static website using MkDocs with the Material theme.

Project Overview

The goal of this project is to provide detailed reviews and notes for several DeepLearning.AI courses. The website includes:

  • Detailed course notes
  • Course reviews
  • Learning paths with flowcharts
  • Tagging system for easy navigation

Features

Explore comprehensive notes and reviews for each course, covering key concepts, practical applications, and major takeaways.

Personal reviews of the courses, highlighting their strengths, weaknesses, and target audience.

Discover the best sequence of courses to take based on your interests and goals. Use our interactive flowcharts to navigate through different learning paths.

Filter courses by topic using our tagging system. Find the content most relevant to your needs quickly and efficiently.

Installation

To run this project locally, follow these steps:

  1. Clone the repository:

    git clone https://github.com/sshkhr/dlai-companion.git
    cd dlai-companion
  2. Create and activate a Python virtual environment:

    python -m venv venv
    source venv/bin/activate  # On Windows use `venv\Scripts\activate`
  3. Install MkDocs and dependencies:

    pip install mkdocs-material[imaging] mkdocs-tags-plugin
  4. Run the MkDocs server:

    mkdocs serve
  5. Open your browser and navigate to http://127.0.0.1:8000 to see the site locally.

Contributing

Contributions are welcome! Please fork this repository and open a pull request to suggest improvements or additions.

License

This project is licensed under the MIT License.

Contact

If you have any questions, suggestions, or feedback, feel free to reach out:

GitHub Twitter LinkedIn

Happy Learning!

Releases

No releases published

Packages

No packages published