Clover collects tons of data about its patients, probably more than most health plans. They may only have 19,000 patients, but they also like to talk about how their data is very wide. Most health plans I've worked with…
It depends on what your goals are. If you'd like to become an ML Engineer or Data Scientist, Tensorflow should be last thing you learn. First, develop a solid foundation in linear algebra and statistics. Then,…
Virtually all data used to predict crimes or recidivism is fraught with human bias, for example. Not sure that we want to reproduce the bias of criminal justice system in any prediction problem involving this type of…
Not talking about the algorithm. It's the data that are biased. The choice of algorithm just determines how interpretable those biases actually are.
This is absolutely one of the reasons. See Google's computer vision system classifying black folks as gorillas: http://blogs.wsj.com/digits/2015/07/01/google-mistakenly-tag...
> If the output examples we are training on are true, the ML algorithm won't adopt any incorrect biases. This is rarely the case when working wtih real data, and thus inspecting whether our models are biased against…
Clover collects tons of data about its patients, probably more than most health plans. They may only have 19,000 patients, but they also like to talk about how their data is very wide. Most health plans I've worked with…
It depends on what your goals are. If you'd like to become an ML Engineer or Data Scientist, Tensorflow should be last thing you learn. First, develop a solid foundation in linear algebra and statistics. Then,…
Virtually all data used to predict crimes or recidivism is fraught with human bias, for example. Not sure that we want to reproduce the bias of criminal justice system in any prediction problem involving this type of…
Not talking about the algorithm. It's the data that are biased. The choice of algorithm just determines how interpretable those biases actually are.
This is absolutely one of the reasons. See Google's computer vision system classifying black folks as gorillas: http://blogs.wsj.com/digits/2015/07/01/google-mistakenly-tag...
> If the output examples we are training on are true, the ML algorithm won't adopt any incorrect biases. This is rarely the case when working wtih real data, and thus inspecting whether our models are biased against…