7 Steps towards Reproducible Research
This workshop aims to take you further down your reproducibility path, by providing concepts and tools you can use in your everyday workflows. It is discipline and experience agnostic, and no coding experience is needed.
We will also examine how Reproducible Research builds business continuity...
Keywords: reproducibility, Reproducibility, reproducible workflows
Resource type: full-course, tutorial
7 Steps towards Reproducible Research
https://amandamiotto.github.io/ReproducibleResearch/
https://dresa.org.au/materials/7-steps-towards-reproducible-research
This workshop aims to take you further down your reproducibility path, by providing concepts and tools you can use in your everyday workflows. It is discipline and experience agnostic, and no coding experience is needed.
We will also examine how Reproducible Research builds business continuity into your research group, how the culture in your institute ecosystem can affect Reproducibility and how you can identify and address risks to your knowledge.
The workshop can be used as self-paced or as an instructor
Amanda Miotto - a.miotto@griffith.edu.au
Amanda Miotto
reproducibility, Reproducibility, reproducible workflows
phd
support
Getting Started with Deep Learning
This lecture provides a high level overview of how you could get started with developing deep learning applications. It introduces deep learning in a nutshell and then provides advice relating to the concepts and skill sets you would need to know and have in order to build a deep learning...
Keywords: Deep learning, Machine learning
Resource type: presentation
Getting Started with Deep Learning
https://doi.org/10.26180/15032688
https://dresa.org.au/materials/getting-started-with-deep-learning
This lecture provides a high level overview of how you could get started with developing deep learning applications. It introduces deep learning in a nutshell and then provides advice relating to the concepts and skill sets you would need to know and have in order to build a deep learning application. The lecture also provides pointers to various resources you could use to gain a stronger foothold in deep learning.
This lecture is targeted at researchers who may be complete beginners in machine learning, deep learning, or even with programming, but who would like to get into the space to build AI systems hands-on.
datascienceplatform@monash.edu
Titus Tang
Deep learning, Machine learning