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Once we have decided on the specifics of our model, we need to do two processes: Compile the model and fit the data to the model.
We can compile the model like so:
`model.compile(optimizer='sgd', l…
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We now have `SuccessiveHalvingClassifier` and `SuccessiveHalvingRegressor` in the `model_selection` to module to perform, well, model selection. This allows doing hyperparameter-tuning by initializing…
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Once we have decided on the specifics of our model, we need to do two processes: Compile the model and fit the data to the model.
We can compile the model like so:
`model.compile(optimizer='sgd', l…
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There’s a line of work out of Michael Jordan’s lab regarding perturbed stochastic gradient descent that allegedly has advantages over SGD:
- Gradient Descent Can Take Exponential Time to Escape Sad…
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I am opening this issue to discuss the implementation of the base SGD in vw_sklearn.
I have opened a pr #2332 for the implementation of the multiclassifier for vw_sklearn and would like to add this b…
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**Problem:** In Stochastic Gradient Descent sometimes the descent will suddenly go unstable even with a step size set as low as you can go (limited by FLoat32 precision). This is not preventable wit…
cems2 updated
4 years ago
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Once we have decided on the specifics of our model, we need to do two processes: Compile the model and fit the data to the model.
We can compile the model like so:
`model.compile(optimizer='sgd', l…
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Add description for gradient descent: batch gradient descent and stochastic gradient descent.
Optionally, also add details about mini-batch GD.
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Once we have decided on the specifics of our model, we need to do two processes: Compile the model and fit the data to the model.
We can compile the model like so:
`model.compile(optimizer='sgd', l…
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I'm wondering whether evotuning should work on _iterations with fixed batch sizes_ (to make visual progress a bit faster) or on _epochs_. From others' empirical experience, iterations with fixed batch…