www.google.com

lynx www.google.com

www.aws.org

do you think www.aws.org runs on aws?

www.allure.com/story/best-sex-tip-by-zodiac-sign/amp?amp_gsa=1&amp_js_v=a6&usqp=mq331AQKKAFQArABIIACAw%3D%3D#amp_tf=From%20%251%24s&aoh=16392879347932&referrer=https%3A%2F%2Fwww.google.com&ampshare=https%3A%2F%2Fwww.allure.com%2Fstory%2Fbest-sex-tip-by-zodiac-sign

For those inter st in the finest writing of all time https://www-allure-com.cdn.ampproject.org/v/s/www.allure.com/story/best-sex-tip-by-zodiac-sign/amp?amp_gsa=1&amp_js_v=a6&usqp=mq331AQKKAFQArABIIACAw%3D%3D#amp_tf=From%20%251%24s&aoh=16392879347932&referrer=https%3A%2F%2Fwww.google.com&ampshare=https%3A%2F%2Fwww.allure.com%2Fstory%2Fbest-sex-tip-by-zodiac-sign

A Neural Network in 11 lines of Python (Part 1) - i am trask

Navigation Brand and toggle get grouped for better mobile display Toggle navigation i am trask Collect the nav links, forms, and other content for toggling Home about Contact /.navbar-collapse /.container Post Header Posted by iamtrask on July 12, 2015 Post Content Summary: I learn best with toy code that I can play with. This tutorial teaches backpropagation via a very simple toy example, a short python implementation. Edit: Some folks have asked about a followup article, and I'm planning to write one. I'll tweet it out when it's complete at @iamtrask . Feel free to follow if you'd be interested in reading it and thanks for all the feedback! X = np.array([ [0,0,1],[0,1,1],[1,0,1],[1,1,1] ]) y = np.array([[0,1,1,0]]).T syn0 = 2*np.random.random((3,4)) - 1 syn1 = 2*np.random.random((4,1)) - 1 for j in xrange(60000): l1 = 1/(1+...

Linked on 2017-01-09 17:40:45 | Similar Links
http://git/gitweb/HASH

' | python - PERCENTAGE_CLEAN ROOT_DIR

A Neural Network in 11 lines of Python - i am trask

Navigation Brand and toggle get grouped for better mobile display Toggle navigation i am trask Collect the nav links, forms, and other content for toggling Home about Contact /.navbar-collapse /.container Post Header Posted by iamtrask on July 12, 2015 Post Content Summary: I learn best with toy code that I can play with. This tutorial teaches backpropagation via a very simple toy example, a short python implementation. Edit: Some folks have asked about a followup article, and I'm planning to write one. I'll tweet it out when it's complete at @iamtrask . Feel free to follow if you'd be interested in reading it and thanks for all the feedback! X = np.array([ [0,0,1],[0,1,1],[1,0,1],[1,1,1] ]) y = np.array([[0,1,1,0]]).T syn0 = 2*np.random.random((3,4)) - 1 syn1 = 2*np.random.random((4,1)) - 1 for j in xrange(60000): l1 = 1/(1+np...

Linked on 2015-07-15 07:25:39 | Similar Links