Samsung Innovation Campus capstone with a team of six: an Android app that simplifies hard Arabic text, adds diacritics and reads it aloud, all on the phone. I worked on the app.
- GitHub
- @mohamedthabet7
- Age
- 19
From first principles
I started with physics simulations: a ball thrown with drag and bounce, a solar system in VPython, and a Pygame window where you slingshot planets around a sun. That turned into machine learning, where I write every algorithm by hand before touching a library. My MNIST network is NumPy matrix maths with hand-derived gradients, and switching ReLU to LeakyReLU took it from 10% to about 80% accuracy. I also do exploratory data analysis on real datasets, such as 187,000 house listings, using Pandas and Seaborn. At Samsung Innovation Campus I studied AI and machine learning, and in a team of six built Bayan, an Android app that simplifies Arabic text for readers with dyslexia.
What I work on
- Machine learning from scratch
- Neural networks
- Backpropagation
- Decision trees
- Clustering
- Regression and statistics
- Exploratory data analysis
- Physics simulation
- NumPy
- Python
Skills
Tools I reach for once I've written the algorithm by hand.
Python
Linear algebra
Calculus for backprop
Probability & entropy
Tools
- Git
- GitHub
- Jupyter Notebook
- pip
- VPython
- Pygame
Stuff I built
Every algorithm written by hand before any library. The first one runs right here: drag on it.
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A Pygame gravity sandbox: drag to aim, release, and Newton's law decides whether the planet orbits, escapes or falls in.
10% → 80%
Test accuracy: ReLU, then LeakyReLU
A two-layer network that reads handwritten digits, written in raw NumPy with hand-derived backpropagation.
Certifications
Artificial Intelligence course, capstone: Bayan
Samsung Innovation Campus