[jupyter] update the invalid links for jupyter notebooks (#538)

Author: sindhuvahinis

jupyter/mxnet/load_your_own_mxnet_bert.ipynb

@@ -461,7 +461,7 @@
     "Q: In what country is Normandy located?\n",
     "A: france\n",
     "\n",
-    "For the full source code,see the [DJL repo](https://github.com/deepjavalibrary/djl/blob/master/examples/src/main/java/ai/djl/examples/inference/BertQaInference.java) and translator implementation [MXNet](https://github.com/deepjavalibrary/djl/blob/master/engines/mxnet/mxnet-model-zoo/src/main/java/ai/djl/mxnet/zoo/nlp/qa/MxBertQATranslator.java) [PyTorch](https://github.com/deepjavalibrary/djl/blob/master/engines/pytorch/pytorch-model-zoo/src/main/java/ai/djl/pytorch/zoo/nlp/qa/PtBertQATranslator.java)."
+    "For the full source code,see the [DJL repo](https://github.com/deepjavalibrary/djl/blob/master/examples/src/main/java/ai/djl/examples/inference/nlp/BertQaInference.java) and translator implementation [MXNet](https://github.com/deepjavalibrary/djl/blob/master/engines/mxnet/mxnet-model-zoo/src/main/java/ai/djl/mxnet/zoo/nlp/qa/MxBertQATranslator.java) [PyTorch](https://github.com/deepjavalibrary/djl/blob/master/engines/pytorch/pytorch-model-zoo/src/main/java/ai/djl/pytorch/zoo/nlp/qa/PtBertQATranslator.java)."
    ]
   }
  ],

jupyter/pytorch/load_your_own_pytorch_bert.ipynb

@@ -417,7 +417,7 @@
     "Q: In what country is Normandy located?\n",
     "A: france\n",
     "\n",
-    "For the full source code, see the [DJL repo](https://github.com/deepjavalibrary/djl/blob/master/examples/src/main/java/ai/djl/examples/inference/BertQaInference.java) and translator implementation [MXNet](https://github.com/deepjavalibrary/djl/blob/master/engines/mxnet/mxnet-model-zoo/src/main/java/ai/djl/mxnet/zoo/nlp/qa/MxBertQATranslator.java) [PyTorch](https://github.com/deepjavalibrary/djl/blob/master/engines/pytorch/pytorch-model-zoo/src/main/java/ai/djl/pytorch/zoo/nlp/qa/PtBertQATranslator.java)."
+    "For the full source code, see the [DJL repo](https://github.com/deepjavalibrary/djl/blob/master/examples/src/main/java/ai/djl/examples/inference/nlp/BertQaInference.java) and translator implementation [MXNet](https://github.com/deepjavalibrary/djl/blob/master/engines/mxnet/mxnet-model-zoo/src/main/java/ai/djl/mxnet/zoo/nlp/qa/MxBertQATranslator.java) [PyTorch](https://github.com/deepjavalibrary/djl/blob/master/engines/pytorch/pytorch-model-zoo/src/main/java/ai/djl/pytorch/zoo/nlp/qa/PtBertQATranslator.java)."
    ]
   }
  ],

jupyter/tensorflow/pneumonia_detection.ipynb

@@ -23,7 +23,7 @@
    "source": [
     "## Preparation\n",
     "\n",
-    "This tutorial requires the installation of Java Kernel. To install the Java Kernel, see the [documentation](https://docs.djl.ai/jupyter/index.html).\n",
+    "This tutorial requires the installation of Java Kernel. To install the Java Kernel, see the [documentation](https://docs.djl.ai/master/docs/demos/jupyter/index.html#install-ijava-kernel-for-jupyter).\n",
     "\n",
     "These are the dependencies we will use:"
    ]

jupyter/transfer_learning_on_cifar10.ipynb

@@ -243,7 +243,7 @@
    "source": [
     "## What's next\n",
     "\n",
-    "1. Try inference using the model you just trained. You can find an airplane image in [test resources](https://github.com/deepjavalibrary/djl/blob/master/examples/src/test/resources/airplane1.png) and follow the inference tutorials in the [Jupyter module](https://docs.djl.ai/docs/demos/jupyter).\n",
+    "1. Try inference using the model you just trained. You can find an airplane image in [test resources](https://github.com/deepjavalibrary/djl/blob/master/examples/src/test/resources/airplane1.png) and follow the inference tutorials in the [Jupyter module](https://docs.djl.ai/master/docs/demos/jupyter/index.html).\n",
     "\n",
     "2. Follow the complete example with multi-GPU support, a validation dataset, and the fit API in the [examples module](https://github.com/deepjavalibrary/djl/tree/master/examples/docs).\n"
    ]

jupyter/tutorial/01_create_your_first_network.ipynb

@@ -8,7 +8,7 @@
     "\n",
     "## Introduction\n",
     "\n",
-    "This is the first part of our [beginner tutorial series](https://docs.djl.ai/docs/demos/jupyter/tutorial) that will take you through creating, training, and running inference on a neural network. In this part, you will learn how to use the built-in `Block` to create your first neural network - a Multilayer Perceptron.\n",
+    "This is the first part of our [beginner tutorial series](https://docs.djl.ai/master/docs/demos/jupyter/tutorial/) that will take you through creating, training, and running inference on a neural network. In this part, you will learn how to use the built-in `Block` to create your first neural network - a Multilayer Perceptron.\n",
     "\n",
     "## Step 1: Setup development environment\n",
     "\n",

jupyter/tutorial/02_train_your_first_model.ipynb

@@ -6,7 +6,7 @@
    "source": [
     "# Train your first model\n",
     "\n",
-    "This is the second of our [beginner tutorial series](https://docs.djl.ai/docs/demos/jupyter/tutorial) that will take you through creating, training, and running inference on a neural network. In this tutorial, you will learn how to train an image classification model that can recognize handwritten digits.\n",
+    "This is the second of our [beginner tutorial series](https://docs.djl.ai/master/docs/demos/jupyter/tutorial/) that will take you through creating, training, and running inference on a neural network. In this tutorial, you will learn how to train an image classification model that can recognize handwritten digits.\n",
     "\n",
     "## Preparation\n",
     "\n",

jupyter/tutorial/03_image_classification_with_your_model.ipynb

@@ -6,7 +6,7 @@
    "source": [
     "# Inference with your model\n",
     "\n",
-    "This is the third and final tutorial of our [beginner tutorial series](https://docs.djl.ai/docs/demos/jupyter/tutorial) that will take you through creating, training, and running inference on a neural network. In this tutorial, you will learn how to execute your image classification model for a production system.\n",
+    "This is the third and final tutorial of our [beginner tutorial series](https://docs.djl.ai/master/docs/demos/jupyter/tutorial/) that will take you through creating, training, and running inference on a neural network. In this tutorial, you will learn how to execute your image classification model for a production system.\n",
     "\n",
     "In the [previous tutorial](02_train_your_first_model.ipynb), you successfully trained your model. Now, we will learn how to implement a `Translator` to convert between POJO and `NDArray` as well as a `Predictor` to run inference.\n",
     "\n",
@@ -179,9 +179,9 @@
    "source": [
     "## Summary\n",
     "\n",
-    "Now, you've successfully built a model, trained it, and run inference. Congratulations on finishing the [beginner tutorial series](https://docs.djl.ai/docs/demos/jupyter/tutorial). After this, you should read our other [examples](https://github.com/deepjavalibrary/djl/tree/master/examples) and [jupyter notebooks](https://docs.djl.ai/docs/demos/jupyter) to learn more about DJL.\n",
+    "Now, you've successfully built a model, trained it, and run inference. Congratulations on finishing the [beginner tutorial series](https://docs.djl.ai/master/docs/demos/jupyter/tutorial/). After this, you should read our other [examples](https://github.com/deepjavalibrary/djl/tree/master/examples) and [jupyter notebooks](https://docs.djl.ai/master/docs/demos/jupyter/index.html) to learn more about DJL.\n",
     "\n",
-    "You can find the complete source code for this tutorial in the [examples project](https://github.com/deepjavalibrary/djl/blob/master/examples/src/main/java/ai/djl/examples/inference/ImageClassification.java)."
+    "You can find the complete source code for this tutorial in the [examples project](https://github.com/deepjavalibrary/djl/blob/master/examples/src/main/java/ai/djl/examples/inference/cv/ImageClassification.java)."
    ]
   }
  ],