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Fixed some typo errors along and one link update
Fixed typo error by removing additional 's' in the name `tf.tensor`s to `tf.tensor`. Also updated the correct link for `tf.data.dataset` API.
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site/en/tutorials/customization/basics.ipynb

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"## Tensors\n",
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"\n",
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"A Tensor is a multi-dimensional array. Similar to NumPy `ndarray` objects, `tf.Tensor` objects have a data type and a shape. Additionally, `tf.Tensor`s can reside in accelerator memory (like a GPU). TensorFlow offers a rich library of operations (for example, `tf.math.add`, `tf.linalg.matmul`, and `tf.linalg.inv`) that consume and produce `tf.Tensor`s. These operations automatically convert built-in Python types. For example:\n"
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"A Tensor is a multi-dimensional array. Similar to NumPy `ndarray` objects, `tf.Tensor` objects have a data type and a shape. Additionally, `tf.Tensor` can reside in accelerator memory (like a GPU). TensorFlow offers a rich library of operations (for example, `tf.math.add`, `tf.linalg.matmul`, and `tf.linalg.inv`) that consume and produce `tf.Tensor`. These operations automatically convert built-in Python types. For example:\n"
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"id": "eBPw8e8vrsom"
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"The most obvious differences between NumPy arrays and `tf.Tensor`s are:\n",
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"The most obvious differences between NumPy arrays and `tf.Tensor` are:\n",
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"1. Tensors can be backed by accelerator memory (like GPU, TPU).\n",
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"2. Tensors are immutable."
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"### NumPy compatibility\n",
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"Converting between a TensorFlow `tf.Tensor`s and a NumPy `ndarray` is easy:\n",
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"Converting between a TensorFlow `tf.Tensor` and a NumPy `ndarray` is easy:\n",
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"\n",
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"* TensorFlow operations automatically convert NumPy ndarrays to Tensors.\n",
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"* NumPy operations automatically convert Tensors to NumPy ndarrays.\n",
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"## Datasets\n",
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"This section uses the [`tf.data.Dataset` API](../../guide/data.ipynb) to build a pipeline for feeding data to your model. `tf.data.Dataset` is used to build performant, complex input pipelines from simple, re-usable pieces that will feed your model's training or evaluation loops."
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"This section uses the [`tf.data.Dataset` API](https://www.tensorflow.org/api_docs/python/tf/data/Dataset) to build a pipeline for feeding data to your model. `tf.data.Dataset` is used to build performant, complex input pipelines from simple, re-usable pieces that will feed your model's training or evaluation loops."
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