Computer Science

Intro

An opiniated guide to Computer Science delivered as weekly videos.

Why?

• We're more and more entangled and dependent of tech, specially now with ML. We need to get more critical mass onboard by teaching people about tech and how to tame it.
• YouTube and other social media/streaming platforms are loaded with ads and click-bait content. We want to create content that tells more with less, go straight on the topic, no bs.
• Demyistifying the (math/coding) background behind our world
• Be practical and fun. Use creative coding as a tool to teach.
• While going through it, give strong background to people be able to do code interviews on big corps b/c the only way to change our world is from inside-out.

Plan/ Syllabus

• Math Background
  • Linear Algebra
  • Calculus
  • Probability and Statistics
  • Math for (Creative) Coding
• Computer Theory
  • Finite Automata
  • Context-Free Grammar
  • Lambda Calculus
  • Turing Machines
• Hardware Architecture Design
  • VMs
  • Assembly
• Operational Systems Design
• Computers as Hackers and Coder Environments
  • Linux
  • Bash
  • Git
  • Vim / Emacs
  • Unix Tools
  • Your dot-files
• Programming
  • Big O
  • Data Structures
    • Lists
    • Trees
    • Graphs
  • Languages
    • Paradigms
      • Imperative
      • Logic
      • Functional
      • OO
      • Metaprogramming
    • C
    • C++
    • Python
  • Design Patterns
• Computer Graphics
  • 3D
  • Animation
  • Games
    • Game Engine Design
• Compilers and Interpreters
• Networking
  • Protocols
  • Sockets
  • P2P
• AI
  • Classical
    • Search
    • Optimization
    • Dynamic Programming
  • ML
    • Neural Networks
      • Deep Learning
        • ConvNets
        • RNN
          • LSTM
        • Transformers
• System Design / Distributed Systems / Software Engineering
• Open Source / Free Software
  • How to contribute
  • Learning by reading code on GH

Applications

• Coding Practice
  • Code Interviews
  • Competitive Programming
• Creative Coding
  • Music
  • Demos
  • Creative Tools as Art Expression
  • Esoteric Languages

References

• Math
  • Calculus
    • http://calculusmadeeasy.org/
    • https://explained.ai/matrix-calculus/
  • Linear Algebra
    • Linear Algebra, Prof. Gilbert Strang
    • Matrix Methods in Data Analysis, Signal Processing and Machine Learning, Prof. Gilbert Strang
• Machine Learning
  • https://mml-book.github.io/
• Deep Learning
  • LeCunn / Casiani 2020 class
• General Software Engineering
  • https://missing.csail.mit.edu/
  • https://opguides.info/
• C++
  • https://isocpp.org/images/uploads/2-Tour-Basics.pdf
• OpenSource Dev
  • http://aosabook.org/en/index.html