Advanced Visualizations with TensorFlow Data Validation Reviews

Muhammad I. · Reviewed أكثر من سنتين ago

Siddhesh N. · Reviewed أكثر من سنتين ago

Dave S. · Reviewed أكثر من سنتين ago

kishore k. · Reviewed أكثر من سنتين ago

done

Kishore K. · Reviewed أكثر من سنتين ago

Rupesh P. · Reviewed أكثر من سنتين ago

julien P. · Reviewed أكثر من سنتين ago

Aljon P. · Reviewed أكثر من سنتين ago

Awesome!

Luis Ángel M. · Reviewed أكثر من سنتين ago

Manuel P. · Reviewed أكثر من سنتين ago

I had to complete the lab locally due to the course being out of date

Matthew V. · Reviewed أكثر من سنتين ago

dailin m. · Reviewed أكثر من سنتين ago

Fahad A. · Reviewed أكثر من سنتين ago

Pritam B. · Reviewed أكثر من سنتين ago

great

Snehal C. · Reviewed أكثر من سنتين ago

Hugo M. · Reviewed أكثر من سنتين ago

Nirmal R. · Reviewed أكثر من سنتين ago

Xiaofeng X. · Reviewed أكثر من سنتين ago

Rana A. · Reviewed أكثر من سنتين ago

Srikeerti V. · Reviewed أكثر من سنتين ago

Omar E. · Reviewed أكثر من سنتين ago

Laszlo S. · Reviewed أكثر من سنتين 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 أكثر من سنتين ago

Abdul Q. · Reviewed أكثر من سنتين ago