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Calculus for AI
in 5 Days

Derivatives, gradients, the chain rule, optimization, and gradient descent — the calculus behind every machine learning algorithm.

5 lessons
Self-paced
~60 min/day
Free forever
No account needed
Start Day 1 — Free → See what's covered
What you'll learn

Calculus for AI in 5 Days

Free 5-day calculus for AI course. Derivatives, gradients, chain rule, optimization, and gradient descent.

  • Compute derivatives using power, product, and chain rules
  • Understand partial derivatives and gradient vectors
  • Apply gradient descent to minimize loss functions
  • Implement backpropagation manually step by step
  • Use Taylor series and L'Hopital's rule in practice
  • Recognize calculus concepts in PyTorch autograd
Gradients
& derivatives
Backprop
from scratch
5
lessons
$0
cost
5-Day Curriculum

Every lesson has a real exercise.
No theory without practice.

01
Derivatives & Rules
Limit definition, power rule, product rule, quotient rule, common functions
power ruleproduct rulelimits
→
02
Chain Rule
Composite functions, chain rule derivation, nested functions, practical examples
chain rulecomposite functionsJacobian
→
03
Partial Derivatives & Gradients
Multivariate functions, partial derivatives, gradient vector, directional derivative
partial derivativesgradientdirectional
→
04
Optimization
Critical points, second derivative test, convexity, constrained optimization, Lagrange multipliers
critical pointsconvexityLagrange
→
05
Gradient Descent & Backprop
SGD, learning rate, momentum, Adam, backpropagation derivation, autograd
SGDAdambackpropagation
→
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Your instructor
B
Bo Peng
Founder, Precision AI Academy

Kaggle Top 200 data scientist. Trained 400+ professionals across universities and federal agencies. Built production systems at scale. This course is practical first — every lesson has working examples and an exercise you can finish in under an hour.

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