关于“Advanced Visualizations with TensorFlow Data Validation”的评价

Muhammad I. · 已于 over 2 years前审核

Siddhesh N. · 已于 over 2 years前审核

Dave S. · 已于 over 2 years前审核

kishore k. · 已于 over 2 years前审核

done

Kishore K. · 已于 over 2 years前审核

Rupesh P. · 已于 over 2 years前审核

julien P. · 已于 over 2 years前审核

Aljon P. · 已于 over 2 years前审核

Awesome!

Luis Ángel M. · 已于 over 2 years前审核

Manuel P. · 已于 over 2 years前审核

I had to complete the lab locally due to the course being out of date

Matthew V. · 已于 over 2 years前审核

dailin m. · 已于 over 2 years前审核

Fahad A. · 已于 over 2 years前审核

Pritam B. · 已于 over 2 years前审核

great

Snehal C. · 已于 over 2 years前审核

Hugo M. · 已于 over 2 years前审核

Nirmal R. · 已于 over 2 years前审核

Xiaofeng X. · 已于 over 2 years前审核

Rana A. · 已于 over 2 years前审核

Srikeerti V. · 已于 over 2 years前审核

Omar E. · 已于 over 2 years前审核

Laszlo S. · 已于 over 2 years前审核

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. · 已于 over 2 years前审核

Abdul Q. · 已于 over 2 years前审核