TF IC notebook

introduction_to_amazon_algorithms/image_classification_tensorflow/Amazon_TensorFlow_Image_Classification.ipynb

@@ -359,7 +359,7 @@
     "            |--ghi.jpg\n",
     "            |--jkl.jpg\n",
     "\n",
-    "We provide tf_flowers dataset as a default dataset for fine-tuning the model. tf_flower comprises images of five types of flowers. The dataset has been downloaded from [TensorFlow](https://www.tensorflow.org/datasets/catalog/tf_flowers). [Apache 2.0 License](https://jumpstart-cache-prod-us-west-2.s3-us-west-2.amazonaws.com/licenses/Apache-License/LICENSE-2.0.txt).\n",
+    "We provide tf_flowers dataset as a default dataset for fine-tuning the model. tf_flower comprises images of five types of flowers. The dataset has been downloaded from [TensorFlow](https://www.tensorflow.org/datasets/catalog/tf_flowers) under [Apache 2.0 License](https://jumpstart-cache-prod-us-west-2.s3-us-west-2.amazonaws.com/licenses/Apache-License/LICENSE-2.0.txt).\n",
     "***"
    ]
   },
@@ -584,7 +584,7 @@
    "source": [
     "## 4.5. Deploy & run Inference on the fine-tuned model\n",
     "***\n",
-    "A trained model does nothing on its own. We now want to use the model to perform inference. For this example, that means predicting the class label of an image. We follow the same steps as in Section 3 [Run inference on the pre-trained model](#3.-Run-inference-on-the-pre-trained-model). We start by retrieving the artifacts for deploying an endpoint. However, instead of base_predictor, we  deploy the `ic_estimator` that we fine-tuned.\n",
+    "A trained model does nothing on its own. We now want to use the model to perform inference. For this example, that means predicting the class label of an image. We follow the same steps as in the [Section 3 - Run inference on the pre-trained model](#3.-Run-inference-on-the-pre-trained-model). We start by retrieving the artifacts for deploying an endpoint. However, instead of base_predictor, we  deploy the `ic_estimator` that we fine-tuned.\n",
     "***"
    ]
   },