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Parallel Computing and Scientific Machine Learning

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Basic Parameter Estimation, Reverse-Mode AD, and Inverse Problems

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Solving Stiff Ordinary Differential Equations

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Forward-Mode Automatic Differentiation (AD) via High Dimensional Algebras

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Ordinary Differential Equations 2: Discretizations and Stability

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Ordinary Differential Equations 1: Applications and Solution Characteristics

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The Different Flavors of Parallelism: Parallel Programming Models

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The Basics of Single Node Parallel Computing

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How Loops Work 2: Computationally-Efficient Discrete Dynamics

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How Loops Work 1: An Introduction to the Theory of Discrete Dynamical Systems

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Introduction to Scientific Machine Learning 1: Deep Learning as Function Approximation

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Introduction to Scientific Machine Learning 2: Physics-Informed Neural Networks

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Optimizing Serial Code in Julia 1: Memory Models, Mutation, and Vectorization

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Getting Started with Julia (for Experienced Programmers)

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Optimizing Serial Code in Julia 2: Type inference, function specialization, and dispatch

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Parallel Computing and Scientific Machine Learning Course: Syllabus

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