Clean up RTD Website #2

advanced_functionality/multi_model_sklearn_home_value/sklearn_multi_model_endpoint_home_value.ipynb

@@ -274,7 +274,7 @@
     "tags": []
    },
    "source": [
-    "# Train multiple house value prediction models\n",
+    "## Train multiple house value prediction models\n",
     "\n",
     "In the follow section, we are setting up the code to train a house price prediction model for each of 4 different cities.\n",
     "\n",
@@ -897,7 +897,7 @@
     "tags": []
    },
    "source": [
-    "# Create the multi-model endpoint with the SageMaker SDK"
+    "## Create the multi-model endpoint with the SageMaker SDK"
    ]
   },
   {
@@ -1064,7 +1064,7 @@
     "tags": []
    },
    "source": [
-    "# Deploy the Multi Model Endpoint\n",
+    "## Deploy the Multi Model Endpoint\n",
     "\n",
     "You need to consider the appropriate instance type and number of instances for the projected prediction workload across all the models you plan to host behind your multi-model endpoint. The number and size of the individual models will also drive memory requirements."
    ]
@@ -1261,7 +1261,7 @@
     "tags": []
    },
    "source": [
-    "# Get predictions from the endpoint\n",
+    "## Get predictions from the endpoint\n",
     "\n",
     "Recall that ```mme.deploy()``` returns a [RealTimePredictor](https://github.com/aws/sagemaker-python-sdk/blob/master/src/sagemaker/predictor.py#L35) that we saved in a variable called ```predictor```.\n",
     "\n",
@@ -1610,4 +1610,4 @@
  },
  "nbformat": 4,
  "nbformat_minor": 5
-}
+}

advanced_functionality/multi_model_xgboost_home_value/xgboost_multi_model_endpoint_home_value.ipynb

@@ -66,7 +66,7 @@
     "tags": []
    },
    "source": [
-    "# Generate synthetic data\n",
+    "## Generate synthetic data\n",
     "\n",
     "The code below contains helper functions to generate synthetic data in the form of a `1x7` numpy array representing the features of a house.\n",
     "\n",
@@ -303,7 +303,7 @@
     "tags": []
    },
    "source": [
-    "# Train multiple house value prediction models\n",
+    "## Train multiple house value prediction models\n",
     "\n",
     "In the follow section, we are setting up the code to train a house price prediction model for each of 4 different cities.\n",
     "\n",
@@ -749,7 +749,7 @@
     "tags": []
    },
    "source": [
-    "# Create the multi-model endpoint with the SageMaker SDK"
+    "## Create the multi-model endpoint with the SageMaker SDK"
    ]
   },
   {
@@ -913,7 +913,7 @@
     "tags": []
    },
    "source": [
-    "# Deploy the Multi Model Endpoint\n",
+    "## Deploy the Multi Model Endpoint\n",
     "\n",
     "You need to consider the appropriate instance type and number of instances for the projected prediction workload across all the models you plan to host behind your multi-model endpoint. The number and size of the individual models will also drive memory requirements."
    ]
@@ -1143,7 +1143,7 @@
     "tags": []
    },
    "source": [
-    "# Get predictions from the endpoint\n",
+    "## Get predictions from the endpoint\n",
     "\n",
     "Recall that ```mme.deploy()``` returns a [RealTimePredictor](https://github.com/aws/sagemaker-python-sdk/blob/master/src/sagemaker/predictor.py#L35) that we saved in a variable called ```predictor```.\n",
     "\n",

aws_marketplace/using_model_packages/auto_insurance/automating_auto_insurance_claim_processing.ipynb

@@ -392,7 +392,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## 5: Cleanup "
+    "## Step 5: Cleanup "
    ]
   },
   {

hyperparameter_tuning/xgboost_direct_marketing/hpo_xgboost_direct_marketing_sagemaker_APIs.ipynb

@@ -4,7 +4,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "# Direct Marketing with Amazon SageMaker XGBoost and Hyperparameter Tuning\n",
+    "# Direct Marketing with Amazon SageMaker XGBoost and Hyperparameter Tuning (SageMaker API)\n",
     "_**Supervised Learning with Gradient Boosted Trees: A Binary Prediction Problem With Unbalanced Classes**_\n",
     "\n",
     "---\n",

hyperparameter_tuning/xgboost_direct_marketing/hpo_xgboost_direct_marketing_sagemaker_python_sdk.ipynb

@@ -4,7 +4,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "# Direct Marketing with Amazon SageMaker XGBoost and Hyperparameter Tuning\n",
+    "# Direct Marketing with Amazon SageMaker XGBoost and Hyperparameter Tuning (SageMaker SDK)\n",
     "_**Supervised Learning with Gradient Boosted Trees: A Binary Prediction Problem With Unbalanced Classes**_\n",
     "\n",
     "---\n",

hyperparameter_tuning/xgboost_random_log/hpo_xgboost_random_log.ipynb

@@ -311,7 +311,7 @@
     "tags": []
    },
    "source": [
-    "# Logarithmic scaling\n",
+    "## Logarithmic scaling\n",
     "\n",
     "In both cases we use logarithmic scaling, which is the scaling type that should be used whenever the order of magnitude is more important that the absolute value. It should be used if a change, say, from 1 to 2 is expected to have a much bigger impact than a change from 100 to 101, due to the fact that the hyperparameter doubles in the first case but not in the latter."
    ]
@@ -350,7 +350,7 @@
     "tags": []
    },
    "source": [
-    "# Random search\n",
+    "## Random search\n",
     "\n",
     "We now start a tuning job using random search. The main advantage of using random search is that this allows us to train jobs with a high level of parallelism"
    ]
@@ -435,7 +435,7 @@
     "tags": []
    },
    "source": [
-    "# Linear scaling\n",
+    "## Linear scaling\n",
     "\n",
     "Let us compare the results with executing a job using linear scaling."
    ]

inference/data_types.rst

@@ -3,20 +3,21 @@ Data Types
 
 Image
 -----
-Coming soon...
 
 .. toctree::
    :maxdepth: 1
 
-
+   ../sagemaker-python-sdk/pytorch_mnist/pytorch_mnist.ipynb
+   ../sagemaker-python-sdk/tensorflow_serving_container/tensorflow_serving_container.ipynb
 
 Tabular
 -------
-Coming soon...
 
 .. toctree::
    :maxdepth: 1
 
+   ../sagemaker-python-sdk/scikit_learn_iris/scikit_learn_estimator_example_with_batch_transform.ipynb
+
 
 Text
 ----
@@ -29,7 +30,8 @@ Text
 
 Time series
 -----------
-Coming soon...
 
 .. toctree::
    :maxdepth: 1
+
+   ../introduction_to_amazon_algorithms/deepar_synthetic/deepar_synthetic.ipynb

introduction_to_amazon_algorithms/blazingtext_text_classification_dbpedia/blazingtext_text_classification_dbpedia.ipynb

@@ -4,7 +4,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## Introduction\n",
+    "# Text Classification using SageMaker BlazingText\n",
     "\n",
     "Text Classification can be used to solve various use-cases like sentiment analysis, spam detection, hashtag prediction etc. This notebook demonstrates the use of SageMaker BlazingText to perform supervised binary/multi class with single or multi label text classification. BlazingText can train the model on more than a billion words in a couple of minutes using a multi-core CPU or a GPU, while achieving performance on par with the state-of-the-art deep learning text classification algorithms. BlazingText extends the fastText text classifier to leverage GPU acceleration using custom CUDA kernels."
    ]

introduction_to_amazon_algorithms/blazingtext_word2vec_subwords_text8/blazingtext_word2vec_subwords_text8.ipynb

@@ -4,7 +4,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## Introduction\n",
+    "# Learning Word2Vec Subword Representations using BlazingText\n",
     "\n",
     "Word2Vec is a popular algorithm used for generating dense vector representations of words in large corpora using unsupervised learning. These representations are useful for many natural language processing (NLP) tasks like sentiment analysis, named entity recognition and machine translation.  \n",
     "\n",

introduction_to_amazon_algorithms/blazingtext_word2vec_text8/blazingtext_word2vec_text8.ipynb

@@ -4,7 +4,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## Introduction\n",
+    "# Learning Word2Vec Word Representations using BlazingText\n",
     "\n",
     "Word2Vec is a popular algorithm used for generating dense vector representations of words in large corpora using unsupervised learning. The resulting vectors have been shown to capture semantic relationships between the corresponding words and are used extensively for many downstream natural language processing (NLP) tasks like sentiment analysis, named entity recognition and machine translation.  "
    ]

introduction_to_amazon_algorithms/deepar_electricity/DeepAR-Electricity.ipynb

@@ -819,7 +819,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "# Additional features\n",
+    "## Additional features\n",
     "\n",
     "We have seen how to prepare a dataset and run DeepAR for a simple example.\n",
     "\n",

introduction_to_amazon_algorithms/imageclassification_caltech/Image-classification-fulltraining-elastic-inference.ipynb

@@ -186,7 +186,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "# Training\n",
+    "## Training\n",
     "Run the training using Amazon SageMaker CreateTrainingJob API"
    ]
   },
@@ -319,7 +319,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "# Deploy The Model\n",
+    "## Deploy The Model\n",
     "\n",
     "***\n",
     "\n",

introduction_to_amazon_algorithms/imageclassification_caltech/Image-classification-fulltraining-highlevel.ipynb

@@ -4,7 +4,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "# End-to-End Multiclass Image Classification Example\n",
+    "# End-to-End Multiclass Image Classification Example with SageMaker SDK and SageMaker Neo\n",
     "1. [Introduction](#Introduction)\n",
     "2. [Prerequisites and Preprocessing](#Prequisites-and-Preprocessing)\n",
     "  1. [Permissions and environment variables](#Permissions-and-environment-variables)\n",
@@ -257,7 +257,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "# Compile\n",
+    "## Compile\n",
     "\n",
     "***\n",
     "\n",
@@ -312,7 +312,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "# Inference\n",
+    "## Inference\n",
     "\n",
     "***\n",
     "\n",

introduction_to_amazon_algorithms/imageclassification_caltech/Image-classification-fulltraining.ipynb

@@ -180,7 +180,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "# Training\n",
+    "## Training\n",
     "Run the training using Amazon sagemaker CreateTrainingJob API"
    ]
   },
@@ -312,7 +312,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "# Deploy The Model\n",
+    "## Deploy The Model\n",
     "\n",
     "***\n",
     "\n",

introduction_to_amazon_algorithms/imageclassification_caltech/Image-classification-incremental-training-highlevel.ipynb

@@ -336,7 +336,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "# Inference\n",
+    "## Inference\n",
     "\n",
     "***\n",
     "\n",

introduction_to_amazon_algorithms/imageclassification_caltech/Image-classification-lst-format-highlevel.ipynb

@@ -367,7 +367,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "# Inference\n",
+    "## Inference\n",
     "\n",
     "***\n",
     "\n",

introduction_to_amazon_algorithms/imageclassification_caltech/Image-classification-transfer-learning-highlevel.ipynb

@@ -4,7 +4,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "# Image classification transfer learning demo\n",
+    "# Image classification transfer learning demo (SageMaker SDK)\n",
     "\n",
     "1. [Introduction](#Introduction)\n",
     "2. [Prerequisites and Preprocessing](#Prequisites-and-Preprocessing)\n",
@@ -253,7 +253,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "# Inference\n",
+    "## Inference\n",
     "\n",
     "***\n",
     "\n",

introduction_to_amazon_algorithms/imageclassification_caltech/Image-classification-transfer-learning.ipynb

@@ -191,7 +191,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "# Training\n",
+    "## Training\n",
     "Run the training using Amazon sagemaker CreateTrainingJob API"
    ]
   },
@@ -323,7 +323,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "# Deploy The Model\n",
+    "## Deploy The Model\n",
     "\n",
     "***\n",
     "\n",

introduction_to_amazon_algorithms/imageclassification_mscoco_multi_label/Image-classification-multilabel-lst.ipynb

@@ -413,7 +413,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "# Inference\n",
+    "## Inference\n",
     "\n",
     "***\n",
     "\n",