Advanced Visualizations with TensorFlow Data Validation Reviews

Siddhesh N. · Reviewed מעל 2 שנים ago

Dave S. · Reviewed מעל 2 שנים ago

kishore k. · Reviewed מעל 2 שנים ago

done

Kishore K. · Reviewed מעל 2 שנים ago

Rupesh P. · Reviewed מעל 2 שנים ago

julien P. · Reviewed מעל 2 שנים ago

Aljon P. · Reviewed מעל 2 שנים ago

Awesome!

Luis Ángel M. · Reviewed מעל 2 שנים ago

Manuel P. · Reviewed מעל 2 שנים ago

I had to complete the lab locally due to the course being out of date

Matthew V. · Reviewed מעל 2 שנים ago

dailin m. · Reviewed מעל 2 שנים ago

Fahad A. · Reviewed מעל 2 שנים ago

Pritam B. · Reviewed מעל 2 שנים ago

great

Snehal C. · Reviewed מעל 2 שנים ago

Hugo M. · Reviewed מעל 2 שנים ago

Nirmal R. · Reviewed מעל 2 שנים ago

Xiaofeng X. · Reviewed מעל 2 שנים ago

Rana A. · Reviewed מעל 2 שנים ago

Srikeerti V. · Reviewed מעל 2 שנים ago

Omar E. · Reviewed מעל 2 שנים ago

Laszlo S. · Reviewed מעל 2 שנים ago

This comment in the notebook is not consistent with the "serving data": "We also have an INT value in our trip seconds, where our schema expected a FLOAT. By making us aware of that difference, TFDV helps uncover inconsistencies in the way the data is generated for training and serving. It's very easy to be unaware of problems like that until model performance suffers, sometimes catastrophically. It may or may not be a significant issue, but in any case this should be cause for further investigation. In this case, we can safely convert INT values to FLOATs, so we want to tell TFDV to use our schema to infer the type. Let's do that now." Actually no anomly is detected for "trip seconds" feature.

Giovanna S. · Reviewed מעל 2 שנים ago

Abdul Q. · Reviewed מעל 2 שנים ago

maziyar a. · Reviewed מעל 2 שנים ago