Media Summary: Time Stamps: 00:00 Welcome! 00:10 Help us add time stamps or captions to this video! See the description for details. Want to ... Transforming one probability distribution to another is a powerful tool in Bayesian inference and machine learning, e.g. ... While Julia is great, there are still a lot of existing useful differentiable

Juliacon 2020 Continuables Jl Python - Detailed Analysis & Overview

Time Stamps: 00:00 Welcome! 00:10 Help us add time stamps or captions to this video! See the description for details. Want to ... Transforming one probability distribution to another is a powerful tool in Bayesian inference and machine learning, e.g. ... While Julia is great, there are still a lot of existing useful differentiable Hard to figure out which one is better between Julia & TimeStamps: 00:00 Welcome! 00:10 Help us add time stamps or captions to this video! See the description for details. Want to ... Lessons learned while achieving a 100x speedup of TrajectoryOptimization.

Contents 00:00 Welcome! 00:38 Class of scattering problems 01:35 Boundary Element Methods (BEM) 02:25 Stating scattering ... Introducing a fresh, new notebook system for rapid prototyping! Pluto understands global references between cells, and reactively ...

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JuliaCon 2020 | StatsModels.jl: Mistakes were made/A `@formula` for success | Dave Kleinschmidt
JuliaCon 2020 | Continuables.jl: Python yield in Julia in blazingly fast | Stephan Sahm
JuliaCon 2020 | DynamicGrids.jl: high-performance spatial simulations in Julia | Rafael Schouten
JuliaCon 2020 | Bijectors.jl: Transforming probability distributions in Julia | Tor Erlend Fjelde
PyCallChainRules.jl: Reusing Differentiable Python Code in Julia | Jayesh K. Gupta | JuliaCon 2022
JuliaCon 2020 | DynamicPPL: Stan-like Speed for Dynamic Probabilistic Models | Mohamed Tarek
Julia vs Python in 2020
JuliaCon 2020 | Julia Track Google Code In and Beyond | Choi Ching Lam
JuliaCon 2020 | Adventures in Avoiding Allocations | Brian Jackson
Handcalcs.jl - Calculations You Can Read and Reuse | Miller | JuliaCon Global 2025
Minisymposium on Partial Differential Equations BEAST.jl | Kristof Cools | JuliaCon 2020
DictArrays.jl: performant type-unstable collections | (Sasha) Plavin | JuliaCon Global 2025
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JuliaCon 2020 | StatsModels.jl: Mistakes were made/A `@formula` for success | Dave Kleinschmidt

JuliaCon 2020 | StatsModels.jl: Mistakes were made/A `@formula` for success | Dave Kleinschmidt

Time Stamps: 00:00 Welcome! 00:10 Help us add time stamps or captions to this video! See the description for details. Want to ...

JuliaCon 2020 | Continuables.jl: Python yield in Julia in blazingly fast | Stephan Sahm

JuliaCon 2020 | Continuables.jl: Python yield in Julia in blazingly fast | Stephan Sahm

When coming from

JuliaCon 2020 | DynamicGrids.jl: high-performance spatial simulations in Julia | Rafael Schouten

JuliaCon 2020 | DynamicGrids.jl: high-performance spatial simulations in Julia | Rafael Schouten

DynamicGrids.

JuliaCon 2020 | Bijectors.jl: Transforming probability distributions in Julia | Tor Erlend Fjelde

JuliaCon 2020 | Bijectors.jl: Transforming probability distributions in Julia | Tor Erlend Fjelde

Transforming one probability distribution to another is a powerful tool in Bayesian inference and machine learning, e.g. ...

PyCallChainRules.jl: Reusing Differentiable Python Code in Julia | Jayesh K. Gupta | JuliaCon 2022

PyCallChainRules.jl: Reusing Differentiable Python Code in Julia | Jayesh K. Gupta | JuliaCon 2022

While Julia is great, there are still a lot of existing useful differentiable

JuliaCon 2020 | DynamicPPL: Stan-like Speed for Dynamic Probabilistic Models | Mohamed Tarek

JuliaCon 2020 | DynamicPPL: Stan-like Speed for Dynamic Probabilistic Models | Mohamed Tarek

We present DynamicPPL.

Julia vs Python in 2020

Julia vs Python in 2020

Hard to figure out which one is better between Julia &

JuliaCon 2020 | Julia Track Google Code In and Beyond | Choi Ching Lam

JuliaCon 2020 | Julia Track Google Code In and Beyond | Choi Ching Lam

TimeStamps: 00:00 Welcome! 00:10 Help us add time stamps or captions to this video! See the description for details. Want to ...

JuliaCon 2020 | Adventures in Avoiding Allocations | Brian Jackson

JuliaCon 2020 | Adventures in Avoiding Allocations | Brian Jackson

Lessons learned while achieving a 100x speedup of TrajectoryOptimization.

Handcalcs.jl - Calculations You Can Read and Reuse | Miller | JuliaCon Global 2025

Handcalcs.jl - Calculations You Can Read and Reuse | Miller | JuliaCon Global 2025

Handcalcs.

Minisymposium on Partial Differential Equations BEAST.jl | Kristof Cools | JuliaCon 2020

Minisymposium on Partial Differential Equations BEAST.jl | Kristof Cools | JuliaCon 2020

Contents 00:00 Welcome! 00:38 Class of scattering problems 01:35 Boundary Element Methods (BEM) 02:25 Stating scattering ...

DictArrays.jl: performant type-unstable collections | (Sasha) Plavin | JuliaCon Global 2025

DictArrays.jl: performant type-unstable collections | (Sasha) Plavin | JuliaCon Global 2025

DictArrays.

Interactive notebooks ~ Pluto.jl | Fons van der Plas | JuliaCon 2020

Interactive notebooks ~ Pluto.jl | Fons van der Plas | JuliaCon 2020

Introducing a fresh, new notebook system for rapid prototyping! Pluto understands global references between cells, and reactively ...