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11_random-initialization_C1W3L09.pptx229 KB12/9/2018 10:10 PM
11_random-initialization.zh-TW.txt5 KB12/9/2018 10:10 PM
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11_random-initialization.mp410,084 KB12/9/2018 10:11 PM
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10_backpropagation-intuition-optional_C1W3L08.pptx502 KB12/9/2018 10:10 PM
10_backpropagation-intuition-optional.zh-TW.txt9 KB12/9/2018 10:10 PM
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10_backpropagation-intuition-optional.mp420,798 KB12/9/2018 10:12 PM
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09_gradient-descent-for-neural-networks_C1W3L07.pptx305 KB12/9/2018 10:10 PM
09_gradient-descent-for-neural-networks.zh-TW.txt6 KB12/9/2018 10:10 PM
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09_gradient-descent-for-neural-networks.mp412,935 KB12/9/2018 10:12 PM
09_gradient-descent-for-neural-networks.ko.txt9 KB12/9/2018 10:10 PM
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08_derivatives-of-activation-functions_C1W3L06.pptx371 KB12/9/2018 10:10 PM
08_derivatives-of-activation-functions.zh-TW.txt5 KB12/9/2018 10:10 PM
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08_derivatives-of-activation-functions.mp49,830 KB12/9/2018 10:11 PM
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07_why-do-you-need-non-linear-activation-functions_C1W3L05.pptx214 KB12/9/2018 10:10 PM
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07_why-do-you-need-non-linear-activation-functions.mp47,563 KB12/9/2018 10:10 PM
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06_activation-functions_C1W3L04.pptx331 KB12/9/2018 10:10 PM
06_activation-functions.zh-TW.txt7 KB12/9/2018 10:10 PM
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06_activation-functions.mp414,937 KB12/9/2018 10:12 PM
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05_explanation-for-vectorized-implementation.zh-TW.txt5 KB12/9/2018 10:10 PM
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05_explanation-for-vectorized-implementation.mp49,938 KB12/9/2018 10:12 PM
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04_vectorizing-across-multiple-examples_C1W3L03.pptx545 KB12/9/2018 10:10 PM
04_vectorizing-across-multiple-examples.zh-TW.txt5 KB12/9/2018 10:10 PM
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04_vectorizing-across-multiple-examples.mp411,859 KB12/9/2018 10:13 PM
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03_computing-a-neural-networks-output.zh-TW.txt8 KB12/9/2018 10:10 PM
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03_computing-a-neural-networks-output.mp413,316 KB12/9/2018 10:12 PM
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02_neural-network-representation_C1W3L02.pptx570 KB12/9/2018 10:10 PM
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01_neural-networks-overview_C1W3L01.pptx168 KB12/9/2018 10:10 PM
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