Daniel Bourke

Hi @ralversity , @cwestergren and @uKnowKlaus , There has been an update to `pd.get_dummies()` to return `bool` dtypes by default (rather than `float` or `int`). You can get the behaviour...

See more of the issue from GregX999: > Looks like the first time the "deprecated methodology" is introduced is in the "Building an End to End CNN Model" (here: https://academy.zerotomastery.io/courses/1240775/lectures/30615045),...

And more again from Pierre: > Of course i can provide you with the number of the video 😉 ...It is the 7th video in Section 5: Computer Vision and...

From Ashik: > It seems like ImageDataGenerator has been depreciated https://www.tensorflow.org/api_docs/python/tf/keras/preprocessing/image/ImageDataGenerator > Leave a note before lecture 116 explaining this > Stick with `tf.data.Dataset` for preprocessing

Hi @piyushongithub, This seems to be an issue with various versions of TensorFlow and the `tf.keras.applications.efficientnet` models. Please see https://github.com/mrdbourke/tensorflow-deep-learning/issues/544 for some potential fixes (namely installing TensorFlow 2.9.0 which seems...

Hi @TarunSehgal27 , Please see here for solutions: https://github.com/mrdbourke/tensorflow-deep-learning/issues/553 Let me know if it helps!

Thank you for helping @SaketMunda and @Encode-0 !

Hi @ivanthecrazy, Investigating this issue myself. I'm going through the following resources: * https://github.com/keras-team/tf-keras/issues/442 * https://discuss.tensorflow.org/t/using-efficientnetb0-and-save-model-will-result-unable-to-serialize-2-0896919-2-1128857-2-1081853-to-json-unrecognized-type-class-tensorflow-python-framework-ops-eagertensor/12518/28 Looks like it's an issue with newer versions of TensorFlow and `tf.keras.applications.efficientnet` models and...

Update: I've confirmed that running notebook 05 works end-to-end with TensorFlow 2.9.0 (as per the links above). Install TensorFlow 2.9.0 with: ```python # Install TensorFlow 2.9.0 to avoid issues (later...

Hi @filipposkar , glad to hear you got it fixed! Looks like this should also be fixed further in upcoming versions of TensorFlow (e.g. 2.13+). For now, it looks like...