Remove unnecessary external tags.

PiperOrigin-RevId: 655664766

site/en/community/contribute/docs_style.md

@@ -65,8 +65,8 @@ repository like this:
 
 This is the preferred approach because this way the links on
 [tensorflow.org](https://www.tensorflow.org),
-[GitHub](https://github.com/tensorflow/docs){:.external} and
-[Colab](https://github.com/tensorflow/docs/tree/master/site/en/guide/bazics.ipynb){:.external}
+[GitHub](https://github.com/tensorflow/docs) and
+[Colab](https://github.com/tensorflow/docs/tree/master/site/en/guide/bazics.ipynb)
 all work. Also, the reader stays in the same site when they click a link.
 
 Note: You should include the file extension—such as `.ipynb` or `.md`—for
@@ -83,10 +83,10 @@ To link to source code, use a link starting with
 by the file name starting at the GitHub root.
 
 When linking off of [tensorflow.org](https://www.tensorflow.org), include a
-`{:.external}` on the Markdown link so that the "external link" symbol is shown.
+`` on the Markdown link so that the "external link" symbol is shown.
 
-* `[GitHub](https://github.com/tensorflow/docs){:.external}` produces
- [GitHub](https://github.com/tensorflow/docs){:.external}
+* `[GitHub](https://github.com/tensorflow/docs)` produces
+ [GitHub](https://github.com/tensorflow/docs)
 
 Do not include URI query parameters in the link:
 

site/en/guide/core/logistic_regression_core.ipynb

@@ -68,9 +68,9 @@
  "id": "DauaqJ7WhIhO"
  },
  "source": [
- "This guide demonstrates how to use the [TensorFlow Core low-level APIs](https://www.tensorflow.org/guide/core) to perform [binary classification](https://developers.google.com/machine-learning/glossary#binary_classification){:.external} with [logistic regression](https://developers.google.com/machine-learning/crash-course/logistic-regression/){:.external}. It uses the [Wisconsin Breast Cancer Dataset](https://archive.ics.uci.edu/ml/datasets/breast+cancer+wisconsin+(original)){:.external} for tumor classification.\n",
+ "This guide demonstrates how to use the [TensorFlow Core low-level APIs](https://www.tensorflow.org/guide/core) to perform [binary classification](https://developers.google.com/machine-learning/glossary#binary_classification) with [logistic regression](https://developers.google.com/machine-learning/crash-course/logistic-regression/). It uses the [Wisconsin Breast Cancer Dataset](https://archive.ics.uci.edu/ml/datasets/breast+cancer+wisconsin+(original)) for tumor classification.\n",
  "\n",
- "[Logistic regression](https://developers.google.com/machine-learning/crash-course/logistic-regression/){:.external} is one of the most popular algorithms for binary classification. Given a set of examples with features, the goal of logistic regression is to output values between 0 and 1, which can be interpreted as the probabilities of each example belonging to a particular class. "
+ "[Logistic regression](https://developers.google.com/machine-learning/crash-course/logistic-regression/) is one of the most popular algorithms for binary classification. Given a set of examples with features, the goal of logistic regression is to output values between 0 and 1, which can be interpreted as the probabilities of each example belonging to a particular class. "
  ]
  },
  {
@@ -81,7 +81,7 @@
  "source": [
  "## Setup\n",
  "\n",
- "This tutorial uses [pandas](https://pandas.pydata.org){:.external} for reading a CSV file into a [DataFrame](https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.html){:.external}, [seaborn](https://seaborn.pydata.org){:.external} for plotting a pairwise relationship in a dataset, [Scikit-learn](https://scikit-learn.org/){:.external} for computing a confusion matrix, and [matplotlib](https://matplotlib.org/){:.external} for creating visualizations."
+ "This tutorial uses [pandas](https://pandas.pydata.org) for reading a CSV file into a [DataFrame](https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.html), [seaborn](https://seaborn.pydata.org) for plotting a pairwise relationship in a dataset, [Scikit-learn](https://scikit-learn.org/) for computing a confusion matrix, and [matplotlib](https://matplotlib.org/) for creating visualizations."
  ]
  },
  {
@@ -128,7 +128,7 @@
  "source": [
  "## Load the data\n",
  "\n",
- "Next, load the [Wisconsin Breast Cancer Dataset](https://archive.ics.uci.edu/ml/datasets/breast+cancer+wisconsin+(original)){:.external} from the [UCI Machine Learning Repository](https://archive.ics.uci.edu/ml/){:.external}. This dataset contains various features such as a tumor's radius, texture, and concavity."
+ "Next, load the [Wisconsin Breast Cancer Dataset](https://archive.ics.uci.edu/ml/datasets/breast+cancer+wisconsin+(original)) from the [UCI Machine Learning Repository](https://archive.ics.uci.edu/ml/). This dataset contains various features such as a tumor's radius, texture, and concavity."
  ]
  },
  {
@@ -156,7 +156,7 @@
  "id": "A3VR1aTP92nV"
  },
  "source": [
- "Read the dataset into a pandas [DataFrame](){:.external} using [`pandas.read_csv`](https://pandas.pydata.org/docs/reference/api/pandas.read_csv.html){:.external}:"
+ "Read the dataset into a pandas [DataFrame]() using [`pandas.read_csv`](https://pandas.pydata.org/docs/reference/api/pandas.read_csv.html):"
  ]
  },
  {
@@ -207,7 +207,7 @@
  "id": "s4-Wn2jzVC1W"
  },
  "source": [
- "Split the dataset into training and test sets using [`pandas.DataFrame.sample`](https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.sample.html){:.external}, [`pandas.DataFrame.drop`](https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.drop.html){:.external} and [`pandas.DataFrame.iloc`](https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.iloc.html){:.external}. Make sure to split the features from the target labels. The test set is used to evaluate your model's generalizability to unseen data."
+ "Split the dataset into training and test sets using [`pandas.DataFrame.sample`](https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.sample.html), [`pandas.DataFrame.drop`](https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.drop.html) and [`pandas.DataFrame.iloc`](https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.iloc.html). Make sure to split the features from the target labels. The test set is used to evaluate your model's generalizability to unseen data."
  ]
  },
  {
@@ -277,7 +277,7 @@
  "\n",
  "This dataset contains the mean, standard error, and largest values for each of the 10 tumor measurements collected per example. The `\"diagnosis\"` target column is a categorical variable with `'M'` indicating a malignant tumor and `'B'` indicating a benign tumor diagnosis. This column needs to be converted into a numerical binary format for model training.\n",
  "\n",
- "The [`pandas.Series.map`](https://pandas.pydata.org/docs/reference/api/pandas.Series.map.html){:.external} function is useful for mapping binary values to the categories.\n",
+ "The [`pandas.Series.map`](https://pandas.pydata.org/docs/reference/api/pandas.Series.map.html) function is useful for mapping binary values to the categories.\n",
  "\n",
  "The dataset should also be converted to a tensor with the `tf.convert_to_tensor` function after the preprocessing is complete."
  ]
@@ -301,7 +301,7 @@
  "id": "J4ubs136WLNp"
  },
  "source": [
- "Use [`seaborn.pairplot`](https://seaborn.pydata.org/generated/seaborn.pairplot.html){:.external} to review the joint distribution of a few pairs of mean-based features from the training set and observe how they relate to the target:"
+ "Use [`seaborn.pairplot`](https://seaborn.pydata.org/generated/seaborn.pairplot.html) to review the joint distribution of a few pairs of mean-based features from the training set and observe how they relate to the target:"
  ]
  },
  {
@@ -343,7 +343,7 @@
  "id": "_8pDCIFjMla8"
  },
  "source": [
- "Given the inconsistent ranges, it is beneficial to standardize the data such that each feature has a zero mean and unit variance. This process is called [normalization](https://developers.google.com/machine-learning/glossary#normalization){:.external}."
+ "Given the inconsistent ranges, it is beneficial to standardize the data such that each feature has a zero mean and unit variance. This process is called [normalization](https://developers.google.com/machine-learning/glossary#normalization)."
  ]
  },
  {
@@ -384,19 +384,19 @@
  "\n",
  "### Logistic regression fundamentals\n",
  "\n",
- "Linear regression returns a linear combination of its inputs; this output is unbounded. The output of a [logistic regression](https://developers.google.com/machine-learning/glossary#logistic_regression){:.external} is in the `(0, 1)` range. For each example, it represents the probability that the example belongs to the _positive_ class.\n",
+ "Linear regression returns a linear combination of its inputs; this output is unbounded. The output of a [logistic regression](https://developers.google.com/machine-learning/glossary#logistic_regression) is in the `(0, 1)` range. For each example, it represents the probability that the example belongs to the _positive_ class.\n",
  "\n",
  "Logistic regression maps the continuous outputs of traditional linear regression, `(-∞, ∞)`, to probabilities, `(0, 1)`. This transformation is also symmetric so that flipping the sign of the linear output results in the inverse of the original probability.\n",
  "\n",
- "Let $Y$ denote the probability of being in class `1` (the tumor is malignant). The desired mapping can be achieved by interpreting the linear regression output as the [log odds](https://developers.google.com/machine-learning/glossary#log-odds){:.external} ratio of being in class `1` as opposed to class `0`:\n",
+ "Let $Y$ denote the probability of being in class `1` (the tumor is malignant). The desired mapping can be achieved by interpreting the linear regression output as the [log odds](https://developers.google.com/machine-learning/glossary#log-odds) ratio of being in class `1` as opposed to class `0`:\n",
  "\n",
  "$$\\ln(\\frac{Y}{1-Y}) = wX + b$$\n",
  "\n",
  "By setting $wX + b = z$, this equation can then be solved for $Y$:\n",
  "\n",
  "$$Y = \\frac{e^{z}}{1 + e^{z}} = \\frac{1}{1 + e^{-z}}$$\n",
  "\n",
- "The expression $\\frac{1}{1 + e^{-z}}$ is known as the [sigmoid function](https://developers.google.com/machine-learning/glossary#sigmoid_function){:.external} $\\sigma(z)$. Hence, the equation for logistic regression can be written as $Y = \\sigma(wX + b)$.\n",
+ "The expression $\\frac{1}{1 + e^{-z}}$ is known as the [sigmoid function](https://developers.google.com/machine-learning/glossary#sigmoid_function) $\\sigma(z)$. Hence, the equation for logistic regression can be written as $Y = \\sigma(wX + b)$.\n",
  "\n",
  "The dataset in this tutorial deals with a high-dimensional feature matrix. Therefore, the above equation must be rewritten in a matrix vector form as follows:\n",
  "\n",
@@ -437,7 +437,7 @@
  "source": [
  "### The log loss function\n",
  "\n",
- "The [log loss](https://developers.google.com/machine-learning/glossary#Log_Loss){:.external}, or binary cross-entropy loss, is the ideal loss function for a binary classification problem with logistic regression. For each example, the log loss quantifies the similarity between a predicted probability and the example's true value. It is determined by the following equation:\n",
+ "The [log loss](https://developers.google.com/machine-learning/glossary#Log_Loss), or binary cross-entropy loss, is the ideal loss function for a binary classification problem with logistic regression. For each example, the log loss quantifies the similarity between a predicted probability and the example's true value. It is determined by the following equation:\n",
  "\n",
  "$$L = -\\frac{1}{m}\\sum_{i=1}^{m}y_i\\cdot\\log(\\hat{y}_i) + (1- y_i)\\cdot\\log(1 - \\hat{y}_i)$$\n",
  "\n",
@@ -471,7 +471,7 @@
  "source": [
  "### The gradient descent update rule\n",
  "\n",
- "The TensorFlow Core APIs support automatic differentiation with `tf.GradientTape`. If you are curious about the mathematics behind the logistic regression [gradient updates](https://developers.google.com/machine-learning/glossary#gradient_descent){:.external}, here is a short explanation:\n",
+ "The TensorFlow Core APIs support automatic differentiation with `tf.GradientTape`. If you are curious about the mathematics behind the logistic regression [gradient updates](https://developers.google.com/machine-learning/glossary#gradient_descent), here is a short explanation:\n",
  "\n",
  "In the above equation for the log loss, recall that each $\\hat{y}_i$ can be rewritten in terms of the inputs as $\\sigma({\\mathrm{X_i}}w + b)$.\n",
  "\n",
@@ -754,7 +754,7 @@
  "\n",
  "For this problem, the FPR is the proportion of malignant tumor predictions amongst tumors that are actually benign. Conversely, the FNR is the proportion of benign tumor predictions among tumors that are actually malignant.\n",
  "\n",
- "Compute a confusion matrix using [`sklearn.metrics.confusion_matrix`](https://scikit-learn.org/stable/modules/generated/sklearn.metrics.confusion_matrix.html#sklearn.metrics.confusion_matrix){:.external}, which evaluates the accuracy of the classification, and use matplotlib to display the matrix:"
+ "Compute a confusion matrix using [`sklearn.metrics.confusion_matrix`](https://scikit-learn.org/stable/modules/generated/sklearn.metrics.confusion_matrix.html#sklearn.metrics.confusion_matrix), which evaluates the accuracy of the classification, and use matplotlib to display the matrix:"
  ]
  },
  {

site/en/guide/core/quickstart_core.ipynb

@@ -68,13 +68,13 @@
  "id": "04QgGZc9bF5D"
  },
  "source": [
- "This quickstart tutorial demonstrates how you can use the [TensorFlow Core low-level APIs](https://www.tensorflow.org/guide/core) to build and train a multiple linear regression model that predicts fuel efficiency. It uses the [Auto MPG](https://archive.ics.uci.edu/ml/datasets/auto+mpg){:.external} dataset which contains fuel efficiency data for late-1970s and early 1980s automobiles.\n",
+ "This quickstart tutorial demonstrates how you can use the [TensorFlow Core low-level APIs](https://www.tensorflow.org/guide/core) to build and train a multiple linear regression model that predicts fuel efficiency. It uses the [Auto MPG](https://archive.ics.uci.edu/ml/datasets/auto+mpg) dataset which contains fuel efficiency data for late-1970s and early 1980s automobiles.\n",
  "\n",
  "You will follow the typical stages of a machine learning process:\n",
  "\n",
  "1. Load the dataset.\n",
  "2. Build an [input pipeline](../data.ipynb).\n",
- "3. Build a multiple [linear regression](https://developers.google.com/machine-learning/glossary#linear-regression){:.external} model.\n",
+ "3. Build a multiple [linear regression](https://developers.google.com/machine-learning/glossary#linear-regression) model.\n",
  "4. Evaluate the performance of the model."
  ]
  },
@@ -114,7 +114,7 @@
  "source": [
  "## Load and preprocess the dataset\n",
  "\n",
- "Next, you need to load and preprocess the [Auto MPG dataset](https://archive.ics.uci.edu/ml/datasets/auto+mpg){:.external} from the [UCI Machine Learning Repository](https://archive.ics.uci.edu/ml/){:.external}. This dataset uses a variety of quantitative and categorical features such as cylinders, displacement, horsepower and weight to predict the fuel efficiencies of automobiles in the late-1970s and early 1980s.\n",
+ "Next, you need to load and preprocess the [Auto MPG dataset](https://archive.ics.uci.edu/ml/datasets/auto+mpg) from the [UCI Machine Learning Repository](https://archive.ics.uci.edu/ml/). This dataset uses a variety of quantitative and categorical features such as cylinders, displacement, horsepower and weight to predict the fuel efficiencies of automobiles in the late-1970s and early 1980s.\n",
  "\n",
  "The dataset contains a few unknown values. Make sure to drop any missing values with `pandas.DataFrame.dropna`, and convert the dataset to a `tf.float32` tensor type with the `tf.convert_to_tensor` and `tf.cast` functions."
  ]
@@ -376,7 +376,7 @@
  "source": [
  "Next, write a training loop to iteratively update your model's parameters by making use of the MSE loss function and its gradients with respect to the input parameters.\n",
  "\n",
- "This iterative method is referred to as [gradient descent](https://developers.google.com/machine-learning/glossary#gradient-descent){:.external}. At each iteration, the model's parameters are updated by taking a step in the opposite direction of their computed gradients. The size of this step is determined by the learning rate, which is a configurable hyperparameter. Recall that the gradient of a function indicates the direction of its steepest ascent; therefore, taking a step in the opposite direction indicates the direction of steepest descent, which ultimately helps to minimize the MSE loss function."
+ "This iterative method is referred to as [gradient descent](https://developers.google.com/machine-learning/glossary#gradient-descent). At each iteration, the model's parameters are updated by taking a step in the opposite direction of their computed gradients. The size of this step is determined by the learning rate, which is a configurable hyperparameter. Recall that the gradient of a function indicates the direction of its steepest ascent; therefore, taking a step in the opposite direction indicates the direction of steepest descent, which ultimately helps to minimize the MSE loss function."
  ]
  },
  {
@@ -434,7 +434,7 @@
  "id": "4mDAAPFqVVgn"
  },
  "source": [
- "Plot the changes in MSE loss over time. Calculating performance metrics on a designated [validation set](https://developers.google.com/machine-learning/glossary#validation-set){:.external} or [test set](https://developers.google.com/machine-learning/glossary#test-set){:.external} ensures the model does not overfit to the training dataset and can generalize well to unseen data."
+ "Plot the changes in MSE loss over time. Calculating performance metrics on a designated [validation set](https://developers.google.com/machine-learning/glossary#validation-set) or [test set](https://developers.google.com/machine-learning/glossary#test-set) ensures the model does not overfit to the training dataset and can generalize well to unseen data."
  ]
  },
  {

site/en/guide/profiler.md

@@ -55,7 +55,7 @@ found.
 When you run profiling with CUDA® Toolkit in a Docker environment or on Linux,
 you may encounter issues related to insufficient CUPTI privileges
 (`CUPTI_ERROR_INSUFFICIENT_PRIVILEGES`). Go to the
-[NVIDIA Developer Docs](https://developer.nvidia.com/nvidia-development-tools-solutions-ERR_NVGPUCTRPERM-permission-issue-performance-counters){:.external}
+[NVIDIA Developer Docs](https://developer.nvidia.com/nvidia-development-tools-solutions-ERR_NVGPUCTRPERM-permission-issue-performance-counters)
 to learn more about how you can resolve these issues on Linux.
 
 To resolve CUPTI privilege issues in a Docker environment, run