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657 tutors in 31 topics, built by 75 teachers. Each one follows a lesson plan its teacher wrote.

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How to read a deep learning paper

How to read a deep learning paper

Read papers in passes, find the real claim and judge the evidence behind itAll levelsAI for research and study4.7(3)41 lessonsSample
Nikolai Sorin$6
Eigenvalues, Eigenvectors and PCA

Eigenvalues, Eigenvectors and PCA

Derive principal component analysis from eigenvectors and use it with full understandingAdvancedMachine learning4.7(3)39 lessonsSample
Katarzyna Wolska$11
Which Maths Do You Need for ML?

Which Maths Do You Need for ML?

Build a realistic maths study plan matched to the kind of AI work you want to doAll levelsMath for AI4.3(3)36 lessonsSample
Leandro Ferraz$5
Reading Statistics in Research and News

Reading Statistics in Research and News

Judge a study or headline claim with a short list of sharp questionsAll levelsData science and statistics4.7(3)34 lessonsSample
Lina Khoury$5
ML Engineering Interview Preparation

ML Engineering Interview Preparation

Practise ML fundamentals, coding, ML system design and behavioural rounds with realistic mock questionsAdvancedAI careers4.7(3)30 lessonsSample
Yohannes Tesfaye$13
Initialisation and normalisation layers

Initialisation and normalisation layers

Understand how weight initialisation and normalisation keep deep networks trainableAdvancedDeep learningNew
Mira Okafor$11
Medical imaging AI: how it is built and checked

Medical imaging AI: how it is built and checked

Understand how imaging models are trained, validated and overseen, for education onlyAll levelsComputer visionNew
Malik Brennan$8
Hyperparameter Tuning on a Budget

Hyperparameter Tuning on a Budget

Search smarter, spend less compute and avoid overfitting your validation setAdvancedMachine learningNew
Lukas Brenner$11
Information Theory and Cross Entropy

Information Theory and Cross Entropy

Understand entropy, cross entropy, KL divergence and perplexity from first principlesAdvancedMath for AINew
Kenta Arai$11
Self supervised and contrastive learning

Self supervised and contrastive learning

Learn how models build useful representations from unlabelled data, and how to test themAdvancedComputer visionNew
Mateo Rojas$11
Numerical Stability: Softmax and Log-Sum-Exp

Numerical Stability: Softmax and Log-Sum-Exp

Stop overflows, underflows and NaNs by computing ML formulas the stable wayAdvancedMath for AINew
Leandro Ferraz$10
Graph neural networks

Graph neural networks

Learn message passing on graphs and build models for nodes, edges and whole graphs without leakageAdvancedDeep learningNew
Nikolai Sorin$11