Clear explanations, formulaic pseudocode, and diagrams to make machine learning math understandable for everyone.
This project contains the final PDF version of my independently researched paper: Formulaic Code for Machine Learning Accessibility.
The goal of this work is to make Machine Learning concepts more accessible, particularly for students and developers without a strong background in calculus. It emphasizes formulaic pseudocode, transparency, and visual explanations over dense traditional notation.
This verison makes for a good novice read. Read the Paper V1
In the case, you consider yourself expecting a more scholarly tone. Let me not dishearten you! Read the Paper V2
- Clear definitions of ML terms (e.g., weights, gradients, optimizers)
- Visual diagrams and graphs to explain complex topics
- Formulaic pseudocode to bridge math and code intuitively
- Accessible explanations for activation functions, loss functions, optimizers, and backpropagation
- Designed for high school students, bootcamp graduates, and independent learners
This work is distributed under the CC BY 4.0 License. You may reuse this material with attribution.
Please cite as: Hadrian Lazic. "Formulaic Pythonic Pseudocode: Making Machine Learning Intuitive and Accessible for Developers and Students." 2025.
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