Merge pull request #2197 from tiruk007:patch-2

PiperOrigin-RevId: 514878662

Author: copybara-github

site/en/guide/tpu.ipynb

@@ -242,7 +242,7 @@
       "source": [
         "### Define a Keras model\n",
         "\n",
-        "Start with a definition of a [`Sequential` Keras model](./sequential_model.ipynb) for image classification on the MNIST dataset. It's no different than what you would use if you were training on CPUs or GPUs. Note that Keras model creation needs to be inside the `Strategy.scope`, so the variables can be created on each TPU device. Other parts of the code are not necessary to be inside the `Strategy` scope."
+        "Start with a definition of a [`Sequential` Keras model](https://www.tensorflow.org/guide/keras/sequential_model) for image classification on the MNIST dataset. It's no different than what you would use if you were training on CPUs or GPUs. Note that Keras model creation needs to be inside the `Strategy.scope`, so the variables can be created on each TPU device. Other parts of the code are not necessary to be inside the `Strategy` scope."
       ]
     },
     {
@@ -555,7 +555,7 @@
         "- [Google Cloud TPU performance guide](https://cloud.google.com/tpu/docs/performance-guide): Enhance Cloud TPU performance further by adjusting Cloud TPU configuration parameters for your application\n",
         "- [Distributed training with TensorFlow](./distributed_training.ipynb): How to use distribution strategies—including `tf.distribute.TPUStrategy`—with examples showing best practices.\n",
         "- TPU embeddings: TensorFlow includes specialized support for training embeddings on TPUs via `tf.tpu.experimental.embedding`. In addition, [TensorFlow Recommenders](https://www.tensorflow.org/recommenders) has `tfrs.layers.embedding.TPUEmbedding`. Embeddings provide efficient and dense representations, capturing complex similarities and relationships between features. TensorFlow's TPU-specific embedding support allows you to train embeddings that are larger than the memory of a single TPU device, and to use sparse and ragged inputs on TPUs.\n",
-        "- [TPU Research Cloud (TRC)] https://sites.research.google/trc/about/: TRC enables researchers to apply for access to a cluster of more than 1,000 Cloud TPU devices.\n"
+        "- [TPU Research Cloud (TRC)](https://sites.research.google/trc/about/): TRC enables researchers to apply for access to a cluster of more than 1,000 Cloud TPU devices.\n"
       ]
     }
   ],