blog_posts: 0af9c315bb
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id | createdDate | title | link | postExcerpt | featuredImageUrl | hash | contributors | modifiedDate | displayDate |
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blog-posts#34-4530 | 2018-05-17 17:29:00 | Perform a large-scale principal component analysis faster using Amazon SageMaker | https://aws.amazon.com/blogs/machine-learning/perform-a-large-scale-principal-component-analysis-faster-using-amazon-sagemaker/ | In this blog post, we conduct a performance comparison for PCA using Amazon SageMaker, Spark ML, and Scikit-Learn on high-dimensional datasets. SageMaker consistently showed faster computational performance. Refer Figures (1) and (2) at the bottom to see the speed improvements. Principal Component Analysis Principal Component Analysis (PCA) is an unsupervised learning algorithm that attempts to [...] | https://d2908q01vomqb2.cloudfront.net/f1f836cb4ea6efb2a0b1b99f41ad8b103eff4b59/2018/05/16/pca-sagemaker-3-2-300x198.jpg | 0af9c315bb | Elena Ehrlich, Hanif Mahboobi | 2018-05-17 17:29:01 | 17 May 2018 |
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