Performing Basic Feature Engineering in Keras Reviews
10353 reviews
Guilherme A. · Reviewed over 3 years ago
Guilherme A. · Reviewed over 3 years ago
Sometimes methods for loading in data is confusing.
Tiego T. · Reviewed over 3 years ago
Xiaohui L. · Reviewed over 3 years ago
Mojtaba G. · Reviewed over 3 years ago
Finally got through the lab after a few attempts, the use of the min max function here, which relies on pandas functionality, is not intuitive. I would have preferred if we instead used a normalization layer which is native to tensorflow. Furthermore, setting random seeds so the work is reproducible/verifiable would be helpful in quantifying the model improvements and incorporating that in the notebook. The instructions for predictions on the test dataset weren't very clear to me. I think there's a missed opportunity here to do things like [(X, y)] = test_ds.take(1), model.predict(X), and likewise feature_layer(X) to help the student understand what's happening under the good. Thank you!
Pritam D. · Reviewed over 3 years ago
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Alexander K. · Reviewed over 3 years ago
Alexander K. · Reviewed over 3 years ago
The instructions around get_scal are not clear, possibly not correct. It says get_scal takes a list of features. In your solution, it takes only one feature. The minmax function hardcodes train[feature]. Given what's previously taught in the class, I was expecting the use of tf.keras.layers.Normalization for this normalization. I think the language/solution could be improved upon. I'm going to try and do this lab on my own to try and get to the end solution. Thank you!
Pritam D. · Reviewed over 3 years ago
Ayan D. · Reviewed over 3 years ago
Liu S. · Reviewed over 3 years ago
GAYATHRI D. · Reviewed over 3 years ago
The lab is just copy and paste from the video
Santiago C. · Reviewed over 3 years ago
Hyungil K. · Reviewed over 3 years ago
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