YoVDO

JAX- Accelerated Machine Learning Research via Composable Function Transformations in Python

Offered By: Fields Institute via YouTube

Tags

Machine Learning Courses Python Courses JAX Courses

Course Description

Overview

Explore accelerated machine learning research through composable function transformations in Python with JAX in this 52-minute seminar presented by Matthew Johnson from Google. Delivered as part of the Machine Learning Advances and Applications Seminar series at the Fields Institute, learn how JAX can enhance and streamline your machine learning workflows, offering powerful tools for researchers and practitioners alike.

Syllabus

JAX: accelerated machine learning research via composable function transformations in Python


Taught by

Fields Institute

Related Courses

JAX Crash Course - Accelerating Machine Learning Code
AssemblyAI via YouTube NFNets - High-Performance Large-Scale Image Recognition Without Normalization
Yannic Kilcher via YouTube Coding a Neural Network from Scratch in Pure JAX - Machine Learning with JAX - Tutorial 3
Aleksa Gordić - The AI Epiphany via YouTube Diffrax - Numerical Differential Equation Solvers in JAX
Fields Institute via YouTube Getting Started with Automatic Differentiation
PyCon US via YouTube