Sagemaker Built-in Algorithms
I am exploring the Sagemaker Built-in algorithms, and I am curious to learn more about the details of the algorithms. However, I am surprised that it is hard to find any references for the research background and implementation details in the numerous documents and tutorials for particular algorithms. If such information exists somewhere, I would highly appreciate a pointer. Thanks a lot in advance!
thanks for your interest in the built-in algorithms! You can find research papers in the documentation of many of them. And documentation page has a section "how it works" explaining the science of every algorithm. For example:
- BlazingText: BlazingText: Scaling and Accelerating Word2Vec using Multiple GPUs, Gupta et Khare
- DeepAR DeepAR: Probabilistic Forecasting with Autoregressive Recurrent Networks, Salinas et al.
- Factorization Machines
- IP Insights
- Linear Learner
- Object Detection (it's an SSD model)
- Random Cut Forest: Robust Random Cut Forest Based Anomaly Detection On Streams, Guha et al
- Semantic Segmentation
Thank you very much for the swift response; the links to the research papers are exactly the type of information I am looking for. However, let's take the paper "BlazingText: Scaling and Accelerating Word2Vec using Multiple GPUs" as an example: Where exactly in the AWS documentation would I find a link to it? Is there a central place to access the foundational papers for the different algorithms?
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