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Free public beta until December 4, 2026: every course is open, no card needed. After that, the first chapter of each course stays free and full access is a one-time $39 ($19 for up to 100 founding members chosen for helpful feedback). Your progress is kept. How the beta works
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ML Information Theory Sprint

Cross-entropy loss on the true label, information gain from a decision-tree split, or softmax with a temperature knob -- three machine-learning ideas, all this course's own vocabulary underneath.

⏱️ 120s
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