AWS Machine Learning Blog

Category: Artificial Intelligence

Use pre-trained models with Apache MXNet

In this blog post, I’ll show you how to use multiple pre-trained models with Apache MXNet. Why would you want to try multiple models? Why not just pick the one with the best accuracy? As we will see later in the blog post, even though these models have been trained on the same data set and optimized for maximum accuracy, they do behave slightly differently on specific images.

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Maximize training performance with Gluon data loader workers

With recent advances in CPU and GPU technology, training complex and deep neural network models in a few hours is within reach for many state of-the-art deep models. However, when you use a system with such high processing throughput potential, the required data for the processing pipeline must be ready before each iteration.

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Use the built-in Amazon SageMaker Random Cut Forest algorithm for anomaly detection

Today, we are launching support for Random Cut Forest (RCF) as the latest built-in algorithm for Amazon SageMaker. RCF is an unsupervised learning algorithm for detecting anomalous data points or outliers within a dataset. This blog post introduces the anomaly detection problem, describes the Amazon SageMaker RCF algorithm, and demonstrates the use of the Amazon […]

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Easily perform facial analysis on live feeds by creating a serverless video analytics environment using Amazon Rekognition Video and Amazon Kinesis Video Streams

In this blog post, we’ll use your webcam on your laptop to send a live feed to an Amazon Kinesis Video Stream. From there, a processor within Amazon Rekognition Video analyzes the feed and compares it to a collection we create. The output matches will get sent to us via an email through an integration with AWS Lambda and Amazon Simple Notification Service (Amazon SNS).

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Build text analytics solutions with Amazon Comprehend and Amazon Relational Database Service

In this blog post, we will show you how to get started building rich text analytics views from your database, without having to learn anything about machine learning for natural language processing models. We’ll do this by leveraging Amazon Comprehend, paired with Amazon Aurora-MySQL and AWS Lambda.

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Build automatic analysis of body language to gauge attention and engagement using Amazon Kinesis Video Streams and Amazon AI Services

This is a guest blog post by Ned T. Sahin, PhD (Brain Power LLC and Harvard University), Runpeng Liu (Brain Power LLC and MIT), Joseph Salisbury, PhD (Brain Power LLC), and Lillian Bu (Brain Power LLC and MIT).  Producers of content (from ads to games to teaching materials) usually judge the success of their content […]

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