Performing Basic Feature Engineering in Keras Reviews

pushpalatha c. · Reviewed מעל 3 שנים ago

Andrea G. · Reviewed מעל 3 שנים ago

Kiana Alessandra V. · Reviewed מעל 3 שנים ago

M R. · Reviewed מעל 3 שנים ago

Mario R. · Reviewed מעל 3 שנים ago

Brandon G. · Reviewed מעל 3 שנים ago

Ang X. · Reviewed מעל 3 שנים ago

Sanjay B. · Reviewed מעל 3 שנים ago

Clive N. · Reviewed מעל 3 שנים ago

Sanjay B. · Reviewed מעל 3 שנים ago

Cheikh T. · Reviewed מעל 3 שנים ago

chhaya s. · Reviewed מעל 3 שנים ago

very difficult.

Steven S. · Reviewed מעל 3 שנים ago

Sergio Adriano L. · Reviewed מעל 3 שנים ago

Brenno M. · Reviewed מעל 3 שנים ago

Takashi D. · Reviewed מעל 3 שנים ago

Nicolo L. · Reviewed מעל 3 שנים ago

Corinna F. · Reviewed מעל 3 שנים ago

Sophie S. · Reviewed מעל 3 שנים ago

Guilherme A. · Reviewed מעל 3 שנים ago

Guilherme A. · Reviewed מעל 3 שנים ago

Sometimes methods for loading in data is confusing.

Tiego T. · Reviewed מעל 3 שנים ago

Xiaohui L. · Reviewed מעל 3 שנים ago

Mojtaba G. · Reviewed מעל 3 שנים 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 מעל 3 שנים ago