Aurélien Geron

Hi @vi3itor , Oh wow, this is really great, thanks so much! 👍 I'm so sorry I missed this PR, I was traveling in France this summer, and I had...

Wow, congratulations Victor, that's really life-changing! 👍 Having a child really changes your life for the better. And it only gets better over time, you're in for a wonderful ride!...

Thanks for your question, and sorry for the late reply. In the previous cell, `Y_proba` is created like this: ```python Y_proba = model.predict(inputs) ``` The `predict()` method returns a NumPy...

Thanks for your feedback, and thanks to everyone for investigating this issue. I added the following comment to the notebook: > **Warning**: the following cell crashes at the end of...

Hi @bafdada, Thanks for your feedback. If `model` is a Keras model, then it has a `predict()` method that takes a single argument `x`, so this should not produce the...

Hi Mahmoud, Thanks for your feedback, and sorry for the late reply. Could you please provide more details? How are you running the Jupyter notebooks? On Colab? Or just viewing...

Hi @aaarbk, Thanks for your feedback. You're absolutely right, I assumed that every district had a unique position, but in practice there are actually duplicates. It's unfortunately too late to...

Hi @YAYAYru , Yes, there are 4 or 5 exercices that don't have a solution yet, sorry about that. I'll try to do them once I'm done writing the 3rd...

Hi @jsukup , thanks for your question. Are you referring to this code example? ```pycon >>> some_data = housing.iloc[:5] >>> some_labels = housing_labels.iloc[:5] >>> some_data_prepared = full_pipeline.transform(some_data) >>> print("Predictions:", lin_reg.predict(some_data_prepared))...

Hi @huang-jl , I can see only two explanations: 1) Perhaps your `full_pipeline` was trained on a part of the dataset that only contained three different categories. Instead, the model...