WIZO
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∇θ σ(z) ∑xᵢwᵢ f(x)=max(0,x)
Open to collabs

WIZO

15 years old

>_

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>_ build_queue --active · 8 entries
wizo.py — neural_core
# ── identity ───────────────────── class Wizo: age = 15 focus = "ML · math · CS" langs = ["ar", "fr", "en"] stack = ["pytorch", "sklearn"] curiosity = float("inf") def __init__(self): self.ml_months = 15 self.deep_learning = True self.classical_ml = True self.shipped = [] # soon ™ def next__(self): return "read papers → build → publish" >>> w = Wizo() >>> w.next__() # 'read papers → build → publish'
# about

Hi, I'm Wizo

I'm a 15yo who enjoys critical and logical thinking, passionate about mathematics, science, computer science, and artificial intelligence. I'm currently learning machine learning and I find it fascinating how data and algorithms can be used to understand and solve complex problems. I speak Arabic, French, and English — three languages that somehow all live in my head at the same time.

You could say I'm a bit of a nerd. I love learning new things, exploring ideas, and understanding how the world works. Curiosity drives a lot of what I do, and I always try to approach challenges with reason, an open mind, and perhaps a little wisdom along the way. Right now I'm 15 months deep into my ML journey — 12 of those were deep learning (started there first, didn't know it was the harder path, the math was rough but I stuck with it), then 3 months of classical ML after. Still going.

ArabicFrenchEnglish PythonHTML/CSS/JSUnityArduinoESP32 PyTorchscikit-learnNumPyPandasMatplotlibSeaborn
15+
Months in ML
3
Languages
4
Notebooks
Curiosity
// ─── journey.trace ─── //
# my journey

How I got here

I didn't plan any of this — I just kept following what interested me and here we are.

// ─── projects.queue ─── //
# projects

What I'm building

Theory is great but at some point you gotta build things. These slots are waiting for me to fill them with stuff that matters.

// ─── neural_net.live ─── //
# playground

Watch a neural net learn

A tiny MLP training live in your browser on the XOR problem — 2 inputs, one hidden layer of 4 neurons, 1 output. Hit train and watch the loss drop and the decision boundary form.

xor_mlp.py — live training epoch 0
lr 0.8
loss:
input layer (2) hidden layer (4) output (1) line thickness = |weight| · opacity = activation
// ─── off_hours.exe ─── //
# off hours

When I'm not nerding out

Even I need breaks sometimes. Here's what I play when I'm not buried in notebooks or code.

// ─── connect.socket ─── //
# connect

Let's talk

Got a project idea, want to collab, or just wanna chat about ML and AI? Hit me up.