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atqy committed Jun 14, 2022
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"cell_type": "markdown",
"metadata": {},
"source": [
"## Image processing\n",
"## Image Processing\n",
"---\n",
"\n",
"SageMaker provides algorithms that are used for image processing.\n",
"\n",
"### image_classification\n",
"### Image Classification\n",
"* [Using SageMaker Image Classification with Amazon Elastic Inference](../introduction_to_amazon_algorithms/imageclassification_caltech/Image-classification-fulltraining-elastic-inference.ipynb)\n",
"* [Image classification training with image format](../introduction_to_amazon_algorithms/imageclassification_caltech/Image-classification-lst-format.ipynb)\n",
"* [End-to-End Incremental Training Image Classification Example](../introduction_to_amazon_algorithms/imageclassification_caltech/Image-classification-incremental-training-highlevel.ipynb)\n",
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"* [End-to-End Multiclass Image Classification Example with SageMaker SDK and SageMaker Neo](../introduction_to_amazon_algorithms/imageclassification_caltech/Image-classification-fulltraining-highlevel.ipynb)\n",
"* [Image classification multi-label classification](../introduction_to_amazon_algorithms/imageclassification_mscoco_multi_label/Image-classification-multilabel-lst.ipynb)\n",
"\n",
"### object_detection\n",
"### Object Detection\n",
"* [Amazon SageMaker Object Detection for Bird Species](../introduction_to_amazon_algorithms/object_detection_birds/object_detection_birds.ipynb)\n",
"\n",
"### semantic_segmentation\n",
"### Semantic Segmentation\n",
"* [Amazon SageMaker Semantic Segmentation Algorithm](../introduction_to_amazon_algorithms/semantic_segmentation_pascalvoc/semantic_segmentation_pascalvoc.ipynb)\n",
"\n"
]
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"cell_type": "markdown",
"metadata": {},
"source": [
"## Text processing\n",
"## Text Processing\n",
"---\n",
"\n",
"SageMaker provides algorithms that are tailored to the analysis of texts and documents used in natural language processing and translation.\n",
"\n",
"### blazingtext\n",
"### BlazingText\n",
"\n",
"* [Text Classification using SageMaker BlazingText](../introduction_to_amazon_algorithms/blazingtext_text_classification_dbpedia/blazingtext_text_classification_dbpedia.ipynb)\n",
"* [Learning Word2Vec Subword Representations using BlazingText](../introduction_to_amazon_algorithms/blazingtext_word2vec_subwords_text8/blazingtext_word2vec_subwords_text8.ipynb)\n",
"* [Learning Word2Vec Word Representations using BlazingText](../introduction_to_amazon_algorithms/blazingtext_word2vec_text8/blazingtext_word2vec_text8.ipynb)\n",
"\n",
"### lda \n",
"### Latent Dirichlet Allocation (LDA) \n",
"\n",
"* [An Introduction to SageMaker LDA](../introduction_to_amazon_algorithms/lda_topic_modeling/LDA-Introduction.ipynb)\n",
"\n",
"### ntm\n",
"### Neural Topic Model (NTM)\n",
"\n",
"* [Amazon SageMaker Neural Topic Model now supports auxiliary vocabulary channel, new topic evaluation metrics, and training subsampling](../scientific_details_of_algorithms/ntm_topic_modeling/ntm_wikitext.ipynb)\n",
"* [Introduction to Basic Functionality of NTM](../introduction_to_amazon_algorithms/ntm_synthetic/ntm_synthetic.ipynb)\n",
"* [An Introduction to SageMaker Neural Topic Model](../introduction_to_applying_machine_learning/ntm_20newsgroups_topic_modeling/ntm_20newsgroups_topic_model.ipynb)\n",
"\n",
"### seq2seq\n",
"### Seq2Seq\n",
"\n",
"* [Machine Translation English-German Example Using SageMaker Seq2Seq](../introduction_to_amazon_algorithms/seq2seq_translation_en-de/SageMaker-Seq2Seq-Translation-English-German.ipynb)\n",
"\n"
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"cell_type": "markdown",
"metadata": {},
"source": [
"## Time series processing\n",
"## Time Series Processing\n",
"---\n",
"\n",
"SageMaker DeepAR algorithm is useful for processing time series data.\n",
"\n",
"### deepar\n",
"### DeepAR\n",
"\n",
"* [Time series forecasting with DeepAR - Synthetic data](../introduction_to_amazon_algorithms/deepar_synthetic/deepar_synthetic.ipynb)\n",
"* [SageMaker/DeepAR demo on electricity dataset](../introduction_to_amazon_algorithms/deepar_electricity/DeepAR-Electricity.ipynb)\n",
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"cell_type": "markdown",
"metadata": {},
"source": [
"## Supervised learning algorithms\n",
"## Supervised Learning Algorithms\n",
"---\n",
"\n",
"Amazon SageMaker provides several built-in general purpose algorithms that can be used for either classification or regression problems.\n",
"\n",
"### factorization_machines\n",
"### Factorization Machines\n",
"\n",
"* [An Introduction to Factorization Machines with MNIST](../introduction_to_amazon_algorithms/factorization_machines_mnist/factorization_machines_mnist.ipynb)\n",
"\n",
"### knn\n",
"### k-Nearest Neighbors (kNN)\n",
"\n",
"* [Multi-Class Classification using Amazon SageMaker k-Nearest-Neighbors (kNN)](../introduction_to_amazon_algorithms/k_nearest_neighbors_covtype/k_nearest_neighbors_covtype.ipynb)\n",
"\n",
"### linear_learner\n",
"### Linear Learner\n",
"\n",
"* [An Introduction to Linear Learner with MNIST](../introduction_to_amazon_algorithms/linear_learner_mnist/linear_learner_mnist.ipynb)\n",
"* [Train Linear Learner model using File System Data Source](../introduction_to_amazon_algorithms/linear_learner_mnist/linear_learner_mnist_with_file_system_data_source.ipynb)\n",
"* [Build multiclass classifiers with Amazon SageMaker linear learner](../scientific_details_of_algorithms/linear_learner_multiclass_classification/linear_learner_multiclass_classification.ipynb)\n",
"* [Fairness Linear Learner in SageMaker](../introduction_to_applying_machine_learning/fair_linear_learner/fair_linear_learner.ipynb)\n",
"\n",
"### xgboost\n",
"### XGBoost\n",
"\n",
"#### Basic\n",
"* [Multiclass classification with Amazon SageMaker XGBoost algorithm](../introduction_to_amazon_algorithms/xgboost_mnist/xgboost_mnist.ipynb)\n",
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"cell_type": "markdown",
"metadata": {},
"source": [
"## Unsupervised learning algorithms\n",
"## Unsupervised Learning Algorithms\n",
"---\n",
"\n",
"Amazon SageMaker provides several built-in algorithms that can be used for a variety of unsupervised learning tasks such as clustering, dimension reduction, pattern recognition, and anomaly detection.\n",
"\n",
"\n",
"### ip_insights\n",
"### IP Insights\n",
"\n",
"* [An Introduction to the Amazon SageMaker IP Insights Algorithm](../introduction_to_amazon_algorithms/ipinsights_login/ipinsights-tutorial.ipynb)\n",
"\n",
"### kmeans\n",
"### K-means\n",
"* [Analyze US census data for population segmentation using Amazon SageMaker](../introduction_to_applying_machine_learning/US-census_population_segmentation_PCA_Kmeans/sagemaker-countycensusclustering.ipynb)\n",
"* [End-to-End Example with Amazon SageMaker K-Means](../sagemaker-python-sdk/1P_kmeans_highlevel/kmeans_mnist.ipynb)\n",
"* [End-to-End Example with Amazon SageMaker K-Means using SageMaker API](../sagemaker-python-sdk/1P_kmeans_lowlevel/kmeans_mnist_lowlevel.ipynb)\n",
"\n",
"### pca\n",
"### Principle Component Analysis (PCA)\n",
"\n",
"* [An Introduction to PCA with MNIST](../introduction_to_amazon_algorithms/pca_mnist/pca_mnist.ipynb)\n",
"\n",
"### rcf\n",
"### Random Cut Forest (RCF)\n",
"\n",
"* [An Introduction to SageMaker Random Cut Forests](../introduction_to_amazon_algorithms/random_cut_forest/random_cut_forest.ipynb)\n",
"\n"
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"cell_type": "markdown",
"metadata": {},
"source": [
"## Feature engineering\n",
"## Feature Engineering\n",
"---\n",
"\n",
"### object2vec\n",
"### Object2Vec\n",
"\n",
"* [Document Embedding with Amazon SageMaker Object2Vec](../introduction_to_applying_machine_learning/object2vec_document_embedding/object2vec_document_embedding.ipynb)\n",
"* [An Introduction to SageMaker ObjectToVec model for MovieLens recommendation](../introduction_to_amazon_algorithms/object2vec_movie_recommendation/object2vec_movie_recommendation.ipynb)\n",
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