Is there documentation or tutorials explaining the procedure/workflow to develop and integrate custom kernels for Gaussian process regression in scikit-learn?
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Why is there a difference between the 2 modules?
When would I use one over the other?
Anything else I should know?
Keras is a standalone high-level API that supports TensorFlow, Theano and CNTK backends. Now, Theano and CNTK are out of development.
tf.keras is the Keras API integrated into TensorFlow 2.
So, if you aim to use TensorFlow as your deep learning framework I recommend using tensorflow.keras for less headache.
Also based on a tweet from François Chollet, the creator of Keras:
We recommend you switch your Keras code to tf.keras.
Both Theano and CNTK are out of development. Meanwhile, as Keras
backends, they represent less than 4% of Keras usage. The other 96% of
users (of which more than half are already on tf.keras) are better
served with tf.keras.
Keras development will focus on tf.keras going forward.
Importantly, we will seek to start developing tf.keras in its own
standalone GitHub repository at keras-team/keras in order to make it
much easier for 3rd party folks to contribute.
I am trying to build a conversational AI chatbot. Since BERT is quite a popular model, I am thinking about using it. I can see that BERT has a pre-trained model for the Question Answering task. Can anyone tell me which version of the BERT model should I use to build a conversation AI? Or Can anyone direct me to the useful resources?
Thanks in advance!
If I developed a testing method for compilers and I want to integrate it into a reproductive build pipeline of a popular Linux distribution, what should I consider?
I have used pre-trained models from OpenVINO for inference. I would like to know how to see the network structure of these models? And if I want to re-train these networks from scratch, can I know from which parent models these pre-trained models were originally derived from?
Information about Intel pre-trained models is available at the following page, “Overview of OpenVINO™ Toolkit Intel's Pre-Trained Models”.
https://docs.openvinotoolkit.org/2020.4/omz_models_intel_index.html
Information about public pre-trained models is available at the following page, “Overview of OpenVINO™ Toolkit Public Models”.
https://docs.openvinotoolkit.org/2020.4/omz_models_public_index.html
DL Workbench can be used to visualize the network structure. DL Workbench is a web-based graphical environment that enables users to visualize, fine-tune, and compare performance of deep learning models. More information about DL Workbench is available at the following page, “Introduction to Deep Learning Workbench”.
https://docs.openvinotoolkit.org/2020.4/workbench_docs_Workbench_DG_Introduction.html
We are trying to figure out how to host and run many of our existing scikit-learn and R models (as is) in GCP. It seems ML Engine is pretty specific to Tensorflow. How can I train a scikit-learn model on Google cloud platform and manage my model if the dataset is too large to pull into datalab? Can I still use ML Engine or is there a different approach most people take?
As an update I was able to get the python script that trains the scikit-learn model to run by submitting it as a training job to ML Engine but haven't found a way to host the pickled model or use it for prediction.
Cloud ML Engine only supports models written in TensorFlow.
If you're using scikit-learn you might want to look at some of the higher level TensorFlow libraries like TF Learn or Keras. They might help migrate your model to TensorFlow in which case you could then use Cloud ML Engine.
It's possible, Cloud ML has this feature from Dec 2017, As of today it is provided as an early access. Basically Cloud ML team is testing this feature but you can also be part of it. More on here.
Use the following command to deploy your scikit-learn models to cloud ml. Please note these parameters may change in future.
gcloud ml-engine versions create ${MODEL_VERSION} --model=${MODEL} --origin="gs://${MODEL_PATH_IN_BUCKET}" --runtime-version="1.2" --framework="SCIKIT_LEARN"
sklearn is now supported on ML Engine.
Here is a fully worked out example of using fully-managed scikit-learn training, online prediction and hyperparameter tuning:
https://github.com/GoogleCloudPlatform/training-data-analyst/blob/master/blogs/sklearn/babyweight_skl.ipynb