6 articles
Complete sicp notes covering procedures and abstraction, data abstraction, modularity, metalinguistic abstraction, and interpreters. Full notes for MIT 6.001 Structure and Interpretation of Computer Programs by Abelson and Sussman.
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Complete cs50 ai notes covering search algorithms, knowledge representation, uncertainty, optimization, machine learning, neural networks, and natural language processing. Full notes for Harvard's CS50 Introduction to AI with Python.
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Complete cs231n notes covering image classification, convolutional networks, training deep networks, RNNs, attention, and generative models. The full CS231n study reference from the Stanford computer vision course.
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Free complete Stanford CS229 machine learning course notes following the official 2026 syllabus. Covers every supervised, unsupervised, and reinforcement learning topic: linear regression, GLMs, SVMs, kernels, neural networks, k-means, EM, RL. The full course reference.
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Complete CS229 lecture notes for the SVM dual formulation. Step-by-step derivation from the primal margin problem to the dual using Lagrange multipliers and KKT conditions, plus why the dual form is what makes the kernel trick possible.
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CS230 lecture notes for Lecture 1 — Andrew Ng's complete introduction to deep learning, covering why deep learning now, the data-performance relationship, generative AI, and how CS230 differs from CS229.
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