http://www.kdnuggets.com/2015/11/google-tensorflow-deep-learning-disappoints.html
http://www.businessinsider.com/what-is-google-tensorflow-2015-11
http://playground.tensorflow.org/#activation=linear&batchSize=10&dataset=circle®Dataset=reg-plane&learningRate=0.03®ularizationRate=0&noise=0&networkShape=4,2&seed=0.98991&showTestData=false&discretize=false&percTrainData=50&x=true&y=true&xTimesY=false&xSquared=false&ySquared=false&cosX=false&sinX=false&cosY=false&sinY=false&collectStats=false&problem=classification&initZero=false&hideText=false
Feature Engineering
https://datasciencedojo.com/data-wrangling-in-r/
https://en.wikipedia.org/wiki/Feature_engineering
For Machine Learning in TensorFlow,
for training the model , the data has to be numeric ( also not categorical variable - not holdeing weightage for that value but just a representation or classification like day of the week)
Also we need to have 5 to 10 samples of the data (possibility), eg. if we are using day of the year , then we may need 5 to 10 years of data , 2 years of data is way too specific.
http://www.businessinsider.com/what-is-google-tensorflow-2015-11
http://playground.tensorflow.org/#activation=linear&batchSize=10&dataset=circle®Dataset=reg-plane&learningRate=0.03®ularizationRate=0&noise=0&networkShape=4,2&seed=0.98991&showTestData=false&discretize=false&percTrainData=50&x=true&y=true&xTimesY=false&xSquared=false&ySquared=false&cosX=false&sinX=false&cosY=false&sinY=false&collectStats=false&problem=classification&initZero=false&hideText=false
Feature Engineering
https://datasciencedojo.com/data-wrangling-in-r/
https://en.wikipedia.org/wiki/Feature_engineering
For Machine Learning in TensorFlow,
for training the model , the data has to be numeric ( also not categorical variable - not holdeing weightage for that value but just a representation or classification like day of the week)
Also we need to have 5 to 10 samples of the data (possibility), eg. if we are using day of the year , then we may need 5 to 10 years of data , 2 years of data is way too specific.
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