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ML Foundations00:42Created in Bhairava Studio
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STUDIO LESSON
01 / 05
ML FOUNDATIONS
Gradient Descent
Gradient Descent
Without Guesswork.
See the idea. Understand the update. Build it in code.
✦
ACADEMY CLASSGenerated by Bhairava Studio
01 · SEE THE PROBLEMFind the lowest point
Find the lowest point
on the loss landscape.
LOSSMODEL WEIGHT
model
02 · UNDERSTAND THE UPDATETake one controlled
Take one controlled
step downhill.
wnext=w−α×∇ loss
Current weightLearning rateDirection of rise
gradient_descent.pyPYTHON
# one gradient descent step
def step(weights, gradient, learning_rate):
return [
weight - learning_rate * gradient[i]
for i, weight in enumerate(weights)
]✦
The code mirrors the visual rule—move every weight opposite to its gradient.
03 · WATCH THE RESULTEvery step brings
Every step brings
the loss lower.
TRAINING RUN● COMPLETE
- step 01loss 0.82
- step 02loss 0.57
- step 03loss 0.31
- step 04loss 0.18
↓
78% lower lossin four controlled updates
CAPTION
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