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Viegas & Wattenburg talk // importance of data for training #29
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shiffman
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This works really well with the material this week about data collection. The reading / videos is a bit overloaded so I may need to consider removing one of the other articles. |
shiffman
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* skeleton of week 4 README * moving and editing material from google doc syllabus * Update 04_diy_neural_network/README.md Co-Authored-By: Ellen Nickles <[email protected]> * Update 04_diy_neural_network/README.md Co-Authored-By: Ellen Nickles <[email protected]> * adding video tutorials, moving new ImageNet works The new ImageNet works suggested by @ellennickles are excellent. I am going to put them with the ImageNet materials from earlier weeks since they match with that material better (this week is about non-image data) and then highlight them in class. * adding Excavating AI work thanks to @ellennickles * while i'm at it, adding Humans of AI by @philippschmitt * adding wattenberg and viegas talk #29 * adding nature of code chapter 10 #9 * removing two articles to reduce load could consider adding these back in later or somewhere else, etc. The nature.com article includes a lot of sophisticated statistics and math concepts / notation so is likely be beyond the scope of this course. cc @lydiajessup * [How to Make A.I. That’s Good for People](https://www.nytimes.com/2018/03/07/opinion/artificial-intelligence-human.html) by Fei-Fei Li * [Estimating the success of re-identifications in incomplete datasets using generative models](https://www.nature.com/articles/s41467-019-10933-3) from nature.com * ready for merge, still lots of work to do
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https://vimeo.com/304131671
Similar to the talk they gave at NYU last year - really helpful for the importance of debugging data.
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