The Math Behind AI

Research Foundations

Geometry, Probability & the Soul of Every Model. "If you want to understand the soul of a model, you must speak the language of Geometry and Probability.".

The promise

Geometry, Probability & the Soul of Every Model. "If you want to understand the soul of a model, you must speak the language of Geometry and Probability.".

Included

Self-paced lessonsHands-on implementationCertificate of completion

What you'll master

Master the **Fisher Information Matrix** as a Riemannian metric

Implement **Natural Gradient Descent (NGD)** and its efficient approximations

Understand the geometry of the probability simplex

Earn a verifiable certificate

Finish the course and claim a shareable AlgoCracked credential with its own public verification page. Here's the certificate you'll earn:

AlgoCracked

Proof of applied skill

Sample certificate

This certifies that

Your Name

has completed the course

The Math Behind AI

AwardedOn course completion

Issued

On completion

Credential ID

CERT-XXXXXXXX

Verify online

algocracked.com/certificate/CERT-XXXXXXXX

★

Preview

Prerequisites

basic programming fluency
comfort with technical self-study
willingness to complete implementation labs

THE FULL BRIEF

How the learning experience works

Information Geometry & Natural Gradients - Master the Fisher Information Matrix as a Riemannian metric Optimal Transport & Wasserstein Geometry - Understand the Monge-Kantorovich problem Differential Geometry & Geometric Deep Learning - Master Equivariant Neural Networks (CNNs as a special case)

Final Deliverable

The Equivariant Transformer.