spMohanty
commented
8 years ago Hi @acabunoc,
I confirm that I will be there at IdeaSquare on both the days of the Sprint between 9-5.
Here is the link to the Github Repository : https://github.com/crowdAI/Knowledge-Base , which will be used to keep track of the progress made during the Sprint.
Also, we would like to change the name and the description to : Name : CrowdAI-KnowledgeBase Description : CrowdAI’s educational vision is to become a great open access learning resource for data analysis and machine learning. To make this happen, we’ll are launching the crowdAI Knowlegde Base, a place where everyone in the community comes together to build high quality resources to help Scientists get started with state of art Machine Learning approaches.
As a part of the Mozilla Global Sprint 2016, we will be focussing on how to get started with trying to classify diseased plant leaves from among 38 crop-disease pairs on a recently released dataset of 54000 images of healthy and diseased plant leaves {PlantVillage} . The goal of the project is also to help anyone get started in similar image classification problems by using state of art deep learning based approaches (with or without access to GPUs).
At the end of the sprint, each of the participants should be able to independently design and train a deep neural network on their own image classification problem (given a set of images, the task at hand is to map every image to a particular class it might belong to).
abbycabs
vijayshankarv
chsasank
[ Project Lead ] @spMohanty , @marcelsalathe [ GitHub Repo ] https://github.com/crowdAI/Knowledge-Base [ Track ] Open Educational Resource: you're collaborating on curriculum or other educational resources, Open Data: you have data others can use and play with during the sprint [ Level ] Beginner [ Timezone ] CEST (Central European Summer Time) [Virtual Participants] 💻 Virtual Participants can connect with us using this link https://vidyoportal.cern.ch/flex.html?roomdirect.html&key=R2WwvMMNooJUWjQQYVy6nXEvQ4
Description
CrowdAI’s educational vision is to become a great open access learning resource for data analysis and machine learning. To make this happen, we’ll are launching the crowdAI Knowlegde Base, a place where everyone in the community comes together to build high quality resources to help Scientists get started with state of art Machine Learning approaches.
As a part of the Mozilla Global Sprint 2016, we will be focussing on how to get started with trying to classify diseased plant leaves from among 38 crop-disease pairs on a recently released dataset of 54000 images of healthy and diseased plant leaves {PlantVillage} . The goal of the project is also to help anyone get started in similar image classification problems by using state of art deep learning based approaches (with or without access to GPUs).
At the end of the sprint, each of the participants should be able to independently design and train a deep neural network on their own image classification problem (given a set of images, the task at hand is to map every image to a particular class it might belong to).
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