JAX: Accelerated Machine Learning Research | SciPy 2020 | VanderPlas
JAX is a system for high-performance machine learning research and numerical computing. It offers the familiarity of Python+NumPy together with hardware acceleration, and it enables the definition and composition of user-wielded function transformations. These transformations include automatic differentiation, automatic vectorized batching, end-to-end compilation (via XLA), parallelizing over multiple accelerators, and more.
JAX had its initial open-source release in December 2018 (https://github.com/google/jax).
This talk will introduce JAX and its core function transformations with a live demo. You’ll learn about JAX’s core design, how it’s powering new research, and how you can start using it too!
*****************
https://twitter.com/enthought
https://www.facebook.com/Enthought/
https://www.linkedin.com/company/enthought
Видео JAX: Accelerated Machine Learning Research | SciPy 2020 | VanderPlas канала Enthought
JAX had its initial open-source release in December 2018 (https://github.com/google/jax).
This talk will introduce JAX and its core function transformations with a live demo. You’ll learn about JAX’s core design, how it’s powering new research, and how you can start using it too!
*****************
https://twitter.com/enthought
https://www.facebook.com/Enthought/
https://www.linkedin.com/company/enthought
Видео JAX: Accelerated Machine Learning Research | SciPy 2020 | VanderPlas канала Enthought
Показать
Комментарии отсутствуют
Информация о видео
Другие видео канала
JAX: accelerated machine learning research via composable function transformations in PythonFrequentism and Bayesianism: What's the Big Deal? | SciPy 2014 | Jake VanderPlasHow to learn any language in six months | Chris Lonsdale | TEDxLingnanUniversityWhat is Automatic Differentiation?Turn any Notebook into a Deployable Dashboard | SciPy 2019 | James BednarBayesian Programming with JAX + NumPyro — Andy KitchenWhy Machines That Bend Are BetterJake VanderPlas - How to Think about Data Visualization - PyCon 2019Graph Convolutional Networks using only NumPyNeurIPS 2020 Tutorial: Deep Implicit LayersSpatial Data Analysis with PySAL Tutorial | SciPy 2020 | Sergio Rey and Elijah KnaapIntroduction to JAXUMAP Uniform Manifold Approximation and Projection for Dimension Reduction | SciPy 2018 |Functional Programming in 40 Minutes • Russ Olsen • GOTO 2018The Bayesian Workflow: Building a COVID-19 Model by Thomas Wiecki [Part 1]Deep Learning: A Crash CourseHigh Dimensional Data Visualization with Clustergrammer2 |SciPy 2020| Nicolas FernandezNeurIPS 2020: JAX Ecosystem MeetupThe Fundamentals of Autograd