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Trade-Offs in Automatic Differentiation: TensorFlow, PyTorch, Jax, and Julia

2 years ago stochasticlifestyle.com

ModelingToolkit, Modelica, and Modia: The Composable Modeling Future in Julia

3 years ago stochasticlifestyle.com

Useful Algorithms That Are Not Optimized by Jax, PyTorch, or TensorFlow

3 years ago stochasticlifestyle.com algorithm

Generalizing Automatic Differentiation to Automatic Sparsity, Uncertainty, Stability, and Parallelism

3 years ago stochasticlifestyle.com

JuliaCall Update: Automated Julia Installation for R Packages

3 years ago stochasticlifestyle.com

How To Train Interpretable Neural Networks That Accurately Extrapolate From Small Data

4 years ago stochasticlifestyle.com

Inexact Models Can Guide Decision Making in Quantitative Systems Pharmacology

4 years ago stochasticlifestyle.com

The Essential Tools of Scientific Machine Learning

5 years ago stochasticlifestyle.com

Why Numba and Cython are not substitutes for Julia

6 years ago stochasticlifestyle.com cython python

Some State of the Art Packages in Julia v1.0

6 years ago stochasticlifestyle.com art

Algorithm efficiency comes from problem information

6 years ago stochasticlifestyle.com algorithm

A Comparison Between Differential Equation Solver Suites In MATLAB, R, Julia, Python, C, Mathematica, Maple, and Fortran - Stochastic Lifestyle

6 years ago stochasticlifestyle.com c python

I like Julia because it scales and is productive

7 years ago stochasticlifestyle.com

A Comparison of Differential Equation Solver Suites

7 years ago stochasticlifestyle.com

DifferentialEquations.jl 2.0: State of the Ecosystem

7 years ago stochasticlifestyle.com