Math for AI
The linear algebra, calculus and probability behind modern models, one idea at a time.
23tutors
7teachers
2free to start
$4 to $11per paid lesson
Math for AI tutors
23 tutors
Backpropagation in practice
Backpropagation in practice
Follow gradients through a real network and fix the training bugs that come from misusing them91 lessonsSampleMira Okafor$7Maths Refresher for Returning AdultsMaths Refresher for Returning Adults
Rebuild the school maths you need for data and AI, calmly and at your own pace73 lessonsSampleKatarzyna WolskaFreeLogs and Exponentials for MLLogs and Exponentials for ML
Get comfortable with exponents and logarithms, the quiet workhorses of machine learning formulas69 lessonsSampleLeandro FerrazFreeMaximum Likelihood, Step by StepMaximum Likelihood, Step by Step
Derive estimates by maximising likelihood and see why common losses are likelihoods in disguise63 lessonsSampleKenta Arai$7Derivatives and Gradients from ScratchDerivatives and Gradients from Scratch
Understand rates of change, derivatives and gradients as the compass that guides learning62 lessonsSampleLeandro Ferraz$4Expected Value and Variance, GentlyExpected Value and Variance, Gently
Understand averages of uncertain outcomes and how much they spread, with dice, games and decisions58 lessonsSampleKenta Arai$4Reading Maths Notation in AI PapersReading Maths Notation in AI Papers
Translate the symbols in machine learning papers into plain words and small examples55 lessonsSampleKatarzyna Wolska$6Probability for Machine LearningProbability for Machine Learning
Use random variables, conditional probability and distributions the way ML models do53 lessonsSampleKenta Arai$7The Chain Rule and BackpropagationThe Chain Rule and Backpropagation
Compute gradients through a network by hand and see exactly what backpropagation does52 lessonsSampleLeandro Ferraz$7Optimisation Basics for MLOptimisation Basics for ML
Understand momentum, adaptive methods, schedules and the shape of loss landscapes50 lessonsSampleLeandro Ferraz$11Matrix Multiplication IntuitionMatrix Multiplication Intuition
See matrix multiplication as many dot products and as a transformation of space50 lessonsSampleKatarzyna Wolska$5Loss Functions and What They RewardLoss Functions and What They Reward
Choose a loss that matches what you actually care about, and know what each one optimises50 lessonsSampleLeandro Ferraz$7Teachers who teach Math for AI
They wrote the lesson plans these tutors follow.
Mira Okafor
I teach how neural networks learn, one small worked example at a timeneural network fundamentals, activation and loss functions, backpropagation9 tutorsSampleKavya Raman
Classical machine learning models, worked through on paper before any codelinear and logistic regression, decision trees, random forests9 tutorsSampleWanjiru Kamau
Study skills coach helping students and families use AI to learn more, not do lessStudy skills, revision and exam preparation, learning to code9 tutorsSampleKenta Arai
Probability for machine learning, plus forecasting and anomaly detectionprobability, expectation and variance, maximum likelihood9 tutorsSampleLina Khoury
Statistics in plain language, from averages to Bayesian reasoningdescriptive statistics, probability, distributions9 tutorsSampleKatarzyna Wolska
Linear algebra for AI, with geometry first and notation secondvectors, matrices, dot products9 tutorsSampleMore in Machine learning
- Machine learning How models learn from data: training, validation, overfitting and the classic methods. 35 tutors
- Data science and statistics Clean data, explore it and draw conclusions you can defend. 30 tutors
- Deep learning Neural networks from single neurons to full training loops, with worked examples. 28 tutors
- NLP Work with text: tokens, embeddings, classification, translation and speech. 14 tutors
- Computer vision Models that see: classification, detection, segmentation and their limits. 14 tutors
- Fine tuning and training Adapt a model to your task with good data and careful training runs. 12 tutors
- MLOps and deployment Ship models to production and keep them healthy: serving, monitoring, updates. 18 tutors