Foundations
Calculus for Machine Learning
Master derivatives, integrals, gradients, and backpropagation for neural networks.
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Library
Practical learning resources covering AI/ML fundamentals, software development, and computer science concepts. My reference materials for understanding systems from first principles. Available for anyone serious about learning.
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Foundations
Linear Algebra for Machine Learning
Deep dive into vectors, matrices, eigenvalues, and SVD that power ML systems.
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Foundations
Probability & Statistics for Machine Learning
Comprehensive guide to Bayes theorem, distributions, MLE, and information theory.
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