2:16:20Basic Parameter Estimation, Reverse-Mode AD, and Inverse Problems
A 136-minute video from Parallel Computing and Scientific Machine Learning: “Basic Parameter Estimation, Reverse-Mode AD, and Inverse Problems”.
Parallel Computing and Scientific Machine Learning
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2:16:20
1:15:44Code Profiling and Optimization (in Julia)
A 76-minute video from Parallel Computing and Scientific Machine Learning: “Code Profiling and Optimization (in Julia)”.
47:58Differentiable Programming Part 1: Reverse-Mode AD Implementation
A 48-minute video from Parallel Computing and Scientific Machine Learning: “Differentiable Programming Part 1: Reverse-Mode AD Implementation”.
1:36:11Differentiable Programming Part 2: Adjoint Derivation for (Neural) ODEs and Nonlinear Solve
A 96-minute video from Parallel Computing and Scientific Machine Learning: “Differentiable Programming Part 2: Adjoint Derivation for (Neural) ODEs and Nonlinear Solve”.
1:51:05Forward-Mode Automatic Differentiation (AD) via High Dimensional Algebras
A 111-minute video from Parallel Computing and Scientific Machine Learning: “Forward-Mode Automatic Differentiation (AD) via High Dimensional Algebras”.
1:46:50From Optimization to Probabilistic Programming
A 107-minute video from Parallel Computing and Scientific Machine Learning: “From Optimization to Probabilistic Programming”.
33:43GPU Programming in Julia
A 34-minute video from Parallel Computing and Scientific Machine Learning: “GPU Programming in Julia”.
34:11Getting Started with Julia (for Experienced Programmers)
A 34-minute video from Parallel Computing and Scientific Machine Learning: “Getting Started with Julia (for Experienced Programmers)”.
1:19:43Global Sensitivity Analysis
A 80-minute video from Parallel Computing and Scientific Machine Learning: “Global Sensitivity Analysis”.
1:03:51How Loops Work 1: An Introduction to the Theory of Discrete Dynamical Systems
A 64-minute video from Parallel Computing and Scientific Machine Learning: “How Loops Work 1: An Introduction to the Theory of Discrete Dynamical Systems”.
48:37How Loops Work 2: Computationally-Efficient Discrete Dynamics
A 49-minute video from Parallel Computing and Scientific Machine Learning: “How Loops Work 2: Computationally-Efficient Discrete Dynamics”.
28:22Introduction to Scientific Machine Learning 1: Deep Learning as Function Approximation
A 28-minute video from Parallel Computing and Scientific Machine Learning: “Introduction to Scientific Machine Learning 1: Deep Learning as Function Approximation”.
29:00Introduction to Scientific Machine Learning 2: Physics-Informed Neural Networks
A 29-minute video from Parallel Computing and Scientific Machine Learning: “Introduction to Scientific Machine Learning 2: Physics-Informed Neural Networks”.
58:59Mixing Differential Equations and Neural Networks for Physics-Informed Learning
A 59-minute video from Parallel Computing and Scientific Machine Learning: “Mixing Differential Equations and Neural Networks for Physics-Informed Learning”.
1:20:57Optimizing Serial Code in Julia 1: Memory Models, Mutation, and Vectorization
A 81-minute video from Parallel Computing and Scientific Machine Learning: “Optimizing Serial Code in Julia 1: Memory Models, Mutation, and Vectorization”.
1:25:00Optimizing Serial Code in Julia 2: Type inference, function specialization, and dispatch
A 85-minute video from Parallel Computing and Scientific Machine Learning: “Optimizing Serial Code in Julia 2: Type inference, function specialization, and dispatch”.
39:50Ordinary Differential Equations 1: Applications and Solution Characteristics
A 40-minute video from Parallel Computing and Scientific Machine Learning: “Ordinary Differential Equations 1: Applications and Solution Characteristics”.
1:31:35Ordinary Differential Equations 2: Discretizations and Stability
A 92-minute video from Parallel Computing and Scientific Machine Learning: “Ordinary Differential Equations 2: Discretizations and Stability”.
43:00Parallel Computing and Scientific Machine Learning Course: Syllabus
A 43-minute video from Parallel Computing and Scientific Machine Learning: “Parallel Computing and Scientific Machine Learning Course: Syllabus”.
28:09Parallel Computing: From SIMD to SIMT
A 28-minute video from Parallel Computing and Scientific Machine Learning: “Parallel Computing: From SIMD to SIMT”.
1:38:29Partial Differential Equations (PDEs), Convolutions, and the Mathematics of Locality
A 98-minute video from Parallel Computing and Scientific Machine Learning: “Partial Differential Equations (PDEs), Convolutions, and the Mathematics of Locality”.
2:21:02Solving Stiff Ordinary Differential Equations
A 141-minute video from Parallel Computing and Scientific Machine Learning: “Solving Stiff Ordinary Differential Equations”.
1:32:54The Basics of Single Node Parallel Computing
A 93-minute video from Parallel Computing and Scientific Machine Learning: “The Basics of Single Node Parallel Computing”.
1:51:30The Different Flavors of Parallelism: Parallel Programming Models
A 112-minute video from Parallel Computing and Scientific Machine Learning: “The Different Flavors of Parallelism: Parallel Programming Models”.
47:16Uncertainty Programming: Differentiable Programming Extended to Uncertainty Quantification
A 47-minute video from Parallel Computing and Scientific Machine Learning: “Uncertainty Programming: Differentiable Programming Extended to Uncertainty Quantification”.