Advanced Visualizations with TensorFlow Data Validation Reviews

Md B. · Reviewed over 2 years ago

Muhammad I. · Reviewed over 2 years ago

Siddhesh N. · Reviewed over 2 years ago

Dave S. · Reviewed over 2 years ago

kishore k. · Reviewed over 2 years ago

done

Kishore K. · Reviewed over 2 years ago

Rupesh P. · Reviewed over 2 years ago

julien P. · Reviewed over 2 years ago

Aljon P. · Reviewed over 2 years ago

Awesome!

Luis Ángel M. · Reviewed over 2 years ago

Manuel P. · Reviewed over 2 years ago

I had to complete the lab locally due to the course being out of date

Matthew V. · Reviewed over 2 years ago

dailin m. · Reviewed over 2 years ago

Fahad A. · Reviewed over 2 years ago

Pritam B. · Reviewed over 2 years ago

great

Snehal C. · Reviewed over 2 years ago

Hugo M. · Reviewed over 2 years ago

Nirmal R. · Reviewed over 2 years ago

Xiaofeng X. · Reviewed over 2 years ago

Rana A. · Reviewed over 2 years ago

Srikeerti V. · Reviewed over 2 years ago

Omar E. · Reviewed over 2 years ago

Laszlo S. · Reviewed over 2 years 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 over 2 years ago