README.md

Computer Science Materials

Courses, articles and research papers with a focus on Computer Science and Math curated by me

Courses

• Introduction to Probability for Computer Scientists.
• A 2020 Vision of Linear Algebra. ( by Gilbert Strang - MIT )
• Introduction to Reinforcement Learning and deep RL. ( Hado van Hasselt, Diana Borsa & Matteo Hessel - DeepMind )

Papers

• Eureka: Human-Level Reward Design via Coding Large Language Models.

Articles

• How AI voice cloning works ?

Download the materials

You can download the courses, articles and papers referenced here by using the download.py script. The script will download all the files; some videos are very long, so it may take a while for everything to finish.

Project structure

This repository contains the required information to start learning about the topics mentioned above. All files for courses and research papers are in a pre-defined json format, those are easily parsed by the scripts in the scripts folder, or directly read by humans. The articles are in the markdown format. The format for the json files is as follows:

Course's json

{
    "course_details": { 
        "source": "who produced the course ( university, company or individual )",
        "title": "title of the course",
        "course_id": "unique identifier for the course - most commonly applied for university courses",
        "professors": "who is teaching the course (may be multiple people)",
        "website_url" : "url to the course website",
        "year": 2020 
    }, 
    "lectures": [
        { 
            "number": 1,
            "description": "lecture title/description",
            "video_url": "https://www.youtube.com/watch?v=2MuDZIAzBMY"
        }
    ]
}

Papers's json

{
    "title": "title for the paper/article",
    "paper_url": "https://arxiv.org/abs/2310.12931",
    "code_repository": "https://github.com/eureka-research/Eureka",
    "project_page": "https://eureka-research.github.io",
    "topics": ["Reinforcement Learning", "Natural Language Processing", "Reward Design", "Language Models"]
}