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4 changes: 2 additions & 2 deletions site/en/guide/core/logistic_regression_core.ipynb
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"\n",
"- The [TensorFlow Core APIs](https://www.tensorflow.org/guide/core) can be used to build machine learning workflows with high levels of configurability\n",
"- Analyzing error rates is a great way to gain more insight about a classification model's performance beyond its overall accuracy score. For more information on classification error rates, visit the following [crash course](https://developers.google.com/machine-learning/crash-course/classification/true-false-positive-negative).\n",
"- Overfitting is another common problem for logistic regression models, though it wasn't a problem for this tutorial. Visit the [Overfit and underfit](overfit_and_underfit.ipynb) tutorial for more help with this.\n",
"- Overfitting is another common problem for logistic regression models, though it wasn't a problem for this tutorial. Visit the [Overfit and underfit](../../tutorials/keras/overfit_and_underfit.ipynb) tutorial for more help with this.\n",
"\n",
"For more examples of using the TensorFlow Core APIs, check out the [guide](https://www.tensorflow.org/guide/core). If you want to learn more about loading and preparing data, see the tutorials on [image data loading](https://www.tensorflow.org/tutorials/load_data/images) or [CSV data loading](https://www.tensorflow.org/tutorials/load_data/csv)."
]
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1 change: 0 additions & 1 deletion site/en/guide/migrate/canned_estimators.ipynb
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