From 4ad2c1a101e7e0868de0d34e6d9a8a84fcee81ea Mon Sep 17 00:00:00 2001 From: atqy Date: Tue, 20 Sep 2022 11:32:06 -0700 Subject: [PATCH] Add visual object detection notebook to README --- README.md | 1 + 1 file changed, 1 insertion(+) diff --git a/README.md b/README.md index ff96af648a..25c9d52f1e 100644 --- a/README.md +++ b/README.md @@ -54,6 +54,7 @@ These examples provide a gentle introduction to machine learning concepts as the - [Population Segmentation of US Census Data using PCA and Kmeans](introduction_to_applying_machine_learning/US-census_population_segmentation_PCA_Kmeans) analyzes US census data and reduces dimensionality using PCA then clusters US counties using KMeans to identify segments of similar counties. - [Document Embedding using Object2Vec](introduction_to_applying_machine_learning/object2vec_document_embedding) is an example to embed a large collection of documents in a common low-dimensional space, so that the semantic distances between these documents are preserved. - [Traffic violations forecasting using DeepAR](introduction_to_applying_machine_learning/deepar_chicago_traffic_violations) is an example to use daily traffic violation data to predict pattern and seasonality to use Amazon DeepAR alogorithm. +- [Visual Inspection Automation with Pre-trained Amazon SageMaker Models](introduction_to_applying_machine_learning/visual_object_detection) is an example for fine-tuning pre-trained Amazon Sagemaker models on a target dataset. ### SageMaker Automatic Model Tuning