Performing Basic Feature Engineering in Keras Ulasan

Prem S. · Diulas hampir 5 tahun lalu

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Nour S. · Diulas hampir 5 tahun lalu

MA Z. · Diulas hampir 5 tahun lalu

MA Z. · Diulas hampir 5 tahun lalu

I think there needs to be a review of the format of this class. The labs and the lectures are not lining up, and if the point of this lab is to learn as we go then there needs to be waaaaaayyyy more time allowed. It takes 5 mins just to set up Juypter Notebooks. On top of all that I think 'labs' that are half filled in and we're just adding a line or two and have little value. A better more educational lab is one where we are giving the tools for solving a problem and then asked to solve the problem. Here we're not giving the tools from solving the problem (at least not in the lectures or labnotebook) and then we're only asked to fill in one or two lines which is not really solving the problem. I would not recommend the learning approach taken in the class to anyone.

Steven H. · Diulas hampir 5 tahun lalu

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Yih-Dar S. · Diulas hampir 5 tahun lalu

Uday B. · Diulas hampir 5 tahun lalu

Rob S. · Diulas hampir 5 tahun lalu

There is an error in the get_scal function: # 'get_scal' function takes a list of numerical features and returns a 'minmax' function # 'Minmax' function itself takes a 'numerical' number from a particular feature and return scaled value of that number. # Scalar def get_scal(feature): # TODO 1d def get_scal(feature): def minmax(x): mini = train[feature].min() maxi = train[feature].max() return (x - mini)/(maxi-mini) return(minmax)

Xingsheng Q. · Diulas hampir 5 tahun lalu

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Paulo C. · Diulas hampir 5 tahun lalu

Ashish P. · Diulas hampir 5 tahun lalu