Calculus for Artificial Intelligence: The Essential Guide

Core Concepts:

• Derivatives: Instantaneous rate of change
• Partial Derivatives: Multivariable functions ∂f/∂x
• Gradients: ∇f(x) = [∂f/∂x₁, ∂f/∂x₂, ...]
• Chain Rule: Foundation of backpropagation

Backpropagation Example:

# Automatic differentiation in PyTorch
import torch

x = torch.tensor(2.0, requires_grad=True)
y = x**3 + 2*x + 1
y.backward()  # Computes dy/dx
print(x.grad)  # 3x² + 2 = 14 when x=2
        

AI Application:

Training loss surfaces typically have 10⁶-10¹² dimensions in modern neural networks

Key Methods:

• Gradient Descent: θ = θ - η∇J(θ)
• Stochastic GD: Batched updates
• Momentum: v = γv + η∇J(θ)
• Adaptive Methods: Adam, RMSprop

Optimizer Comparison:

Optimizer	Update Rule	Best For
SGD	θ = θ - η∇J	Simple convex problems
Adam	m = β₁m + (1-β₁)∇J v = β₂v + (1-β₂)(∇J)² θ = θ - ηm/(√v + ε)	Deep neural networks

Performance Insight:

Adam optimizer typically converges 3-5x faster than vanilla SGD for deep learning tasks

Key Applications:

• Probability Densities: P(a ≤ X ≤ b) = ∫f(x)dx
• Expected Values: E[X] = ∫xf(x)dx
• Bayesian Inference: Posterior ∝ Likelihood × Prior
• VAEs: KL divergence integrals

Monte Carlo Integration:

# Estimating π via Monte Carlo
import numpy as np

samples = np.random.rand(10000, 2)
inside = np.sum(samples[:,0]**2 + samples[:,1]**2 <= 1)
pi_estimate = 4 * inside / len(samples)  # ≈ 3.141...
        

AI Connection:

Modern probabilistic AI models use 10⁶-10⁹ dimensional integrals approximated computationally