AWS AI Blog

A/B Testing at Scale – Amazon Machine Learning Research

by Guy Ernest | on | Permalink | Comments |  Share

This week, Amazon presented an academic paper at KDD 2017, the prestigious machine learning and big data conference. The paper shows Amazon’s research into tools that help us measure customers’ satisfaction and better learn how we can implement ideas that delight them. Specifically, we show an efficient bandit algorithm for multivariate testing, where one seeks […]

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Apache MXNet Release Candidate Introduces Support for Apple’s Core ML and Keras v1.2

by Cynthya Peranandam | on | Permalink | Comments |  Share

Apache MXNet is an effort undergoing incubation at the Apache Software Foundation (ASF). Last week, the MXNet community introduced a release candidate for MXNet v0.11.0, its first as an incubating project, and the community is now voting on whether to accept this candidate as a release. It includes the following major feature enhancements: A Core […]

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Build Your Own Face Recognition Service Using Amazon Rekognition

by Christian Petters | on | Permalink | Comments |  Share

Amazon Rekognition is a service that makes it easy to add image analysis to your applications. It’s based on the same proven, highly scalable, deep learning technology developed by Amazon’s computer vision scientists to analyze billions of images daily for Amazon Prime Photos. Facial recognition enables you to find similar faces in a large collection […]

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Estimating the Location of Images Using Apache MXNet and Multimedia Commons Dataset on AWS EC2

by Jaeyoung Choi and Kevin Li | on | Permalink | Comments |  Share

This is a guest post by Jaeyoung Choi of the International Computer Science Institute and Kevin Li of the University of California, Berkeley. This project demonstrates how academic researchers can leverage our AWS Cloud Credits for Research Program to support their scientific breakthroughs. Modern mobile devices can automatically assign geo-coordinates to images when you take pictures of […]

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Analyze Emotion in Video Frame Samples Using Amazon Rekognition on AWS

by Cyrus Wong | on | Permalink | Comments |  Share

This guest post is by AWS Community Hero Cyrus Wong. Cyrus is a Data Scientist at the Hong Kong Vocational Education (Lee Wai Lee) Cloud Innovation Centre. He has achieved all 7 AWS Certifications and enjoys sharing his AWS knowledge with others through open-source projects, blog posts, and events. HowWhoFeelInVideo is an application that analyzes […]

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Exploiting the Unique Features of the Apache MXNet Deep Learning Framework with a Cheat Sheet

by Sunil Mallya | on | Permalink | Comments |  Share

Apache MXNet (incubating) is a full-featured, highly scalable deep learning framework that supports creating and training state-of-the-art deep learning models. With it, you can create convolutional neural networks (CNNs), long short-term memory networks (LSTMs), and others. It supports a variety of languages, including, but not limited to, Python, Scala, R, and Julia. In this post, we showcase […]

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Create a Serverless Solution for Video Frame Analysis and Alerting

by Moataz Anany | on | Permalink | Comments |  Share

Imagine capturing frames off of live video streams, identifying objects within the frames, and then triggering actions or notifications based on the identified objects. Now imagine accomplishing all of this with low latency and without a single server to manage In this post, I present a serverless solution that uses Amazon Rekognition and other AWS […]

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The AWS Deep Learning AMI for Ubuntu is Now Available with CUDA 8, Ubuntu 16, and the Latest Versions of Deep Learning Frameworks

by Cynthya Peranandam | on | Permalink | Comments |  Share

The AWS Deep Learning AMI lets you build and scale deep learning applications in the cloud, at any scale. The AMI comes pre-installed with popular deep learning frameworks, to let you to train sophisticated, custom AI models, experiment with new algorithms, or to learn new skills and techniques. The latest release of the AWS Deep […]

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Train Neural Machine Translation Models with Sockeye

by Felix Hieber and Tobias Domhan | on | Permalink | Comments |  Share

Have you ever wondered how you can use machine learning (ML) for translation? With our new framework, Sockeye, you can model machine translation (MT) and other sequence-to-sequence tasks. Sockeye, which is built on Apache MXNet, does most of the heavy lifting for building, training, and running state-of-the-art sequence-to-sequence models. In natural language processing (NLP), many […]

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Building a Reliable Text-to-Speech Service with Amazon Polly

by Yiannis Philipopoulos | on | Permalink | Comments |  Share

Listen to this post Voiced by Amazon Polly This is a guest post by Yiannis Philipopoulos, a Software Developer at Bandwidth. In Yiannis’ words: “Bandwidth’s solutions are shaping the future of how we connect with voice and messaging for mobile apps and large-scale, enterprise-level solutions. At the core of Bandwidth’s business-grade Communications Platform as a […]

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