Software Engineering & Testing
Notes on software testing concepts and techniques.
Machine Learning
Notes on machine learning concepts and algorithms.
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Introduction to Machine Learning
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Linear Algebra for Machine Learning
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The Perceptron Algorithm
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Linear Regression
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Basis Functions for Non-Linear Problems
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Introduction to Neural Networks
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Function Approximation and the Curse of Dimensionality
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Introduction to Deep Learning
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Recurrent Neural Networks and Attention Mechanisms
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Manifold Learning, Autoencoders, and Generative Models
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Kernel Methods and the Kernel Trick
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Probability for Machine Learning
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Frequentist vs. Bayesian Statistics
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Introduction to Linear Classifiers
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Support Vector Machines
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Model Selection and Estimating Generalization Performance
Linear Algebra for Machine Learning
Linear algebra foundations for machine learning.
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The Linear Algebra of Machine Learning: A Roadmap
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Vectors and Vector Spaces
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Matrices and Data Representation
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Dot Products and Vector Norms
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Matrix Multiplication and Linear Transformations
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Systems of Linear Equations
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Matrix Inverse, Linear Independence, and Rank
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Eigenvectors and Eigenvalues
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Eigendecomposition of a Matrix
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Singular Value Decomposition (SVD)
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Application: Principal Component Analysis (PCA)
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Vector Projections and Orthogonality
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Application: Linear Regression and the Normal Equation
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Matrix Calculus: Gradients with Vectors and Matrices
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Conclusion: The Linear Algebra Foundation
Reinforcement Learning
Notes on reinforcement learning algorithms.