Use relative link for Overfit and underfit tutorial in Logistic regression for binary classification with Core APIs guide

Author: 8bitmp3

site/en/guide/core/logistic_regression_core.ipynb

@@ -848,7 +848,7 @@
         "\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](https://www.tensorflow.org/tutorials/keras/overfit_and_underfit) 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)."
       ]