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lec3_HowtoMinimizeTensorflow끄적끄적/기초배우기(sung kim) 2020. 4. 27. 03:23
''' What cost(W) looks like? Gradient descent algorithm(경사를 따라 감소하는 알고리즘) it is used many minimizaion problems For a given cost function cost(W,b),it will find W,b to minimize cost it can be applied to more general function How it works? How would you find the lowest point? 1. Start with initial guesses - Start at 0,0(or any other value) - Keeping changing W and b alittle bit to try and reduce c..
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lec2_linearRegressionTensorflow끄적끄적/기초배우기(sung kim) 2020. 4. 26. 14:48
''' Regression(data) X(feature data) | Y 1 |1 2 |2 3 |3 (Linear)Hypothesis(선형가설) 1. H(x) = Wx + b 2. which hypothesis is better? Cost(loss) function : How fit the line to our (training)data (H(x)-y)^2 --> give more panalty when it get more gap! you don t need to consider about sign(minus/plus) for i in len(data): cost = ((H(x(i))-y(i))**2)/len(data) Goal : Minimize cost cost
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lec1_basicTensorflow끄적끄적/기초배우기(sung kim) 2020. 4. 26. 14:41
''' What is ML : Automatic driving을 구현하기 위해서는 조건들이 너무 많이 필요하다. => 개발자가 정하지 않고 기계가 스스로 조건들을 학습하는 방법을 생각함 Supervised learning : learning with labeled examples - training set Most common problem type in ML ex)distinguish cat and dog Predicting final exam score Email filter Training data set Unsupervised learning:un-labeled data - Google news grouping - Word clustering Types of supervised learning 1..