Deep Learning on AWS at NVIDIA’s GPU Technology Conference, GTC 2017

by Joseph Spisak | on | Permalink | Comments |  Share

This year at NVIDIA’s GPU Technology Conference, AWS is hosting several tech sessions ranging from how to get started with Apache MXNet to running deep learning on IoT devices on the edge. If you’re in Silicon Valley the week of May 8, we hope that you’ll join us for the following sessions. An Introduction to […]

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Fast CNN Tuning with AWS GPU Instances and SigOpt

by Steven Tartakovsky | on | Permalink | Comments |  Share

By Steven Tartakovsky, Michael McCourt, and Scott Clark of SigOpt Compared with traditional machine learning models, neural networks are computationally more complex and introduce many additional parameters. This often prevents machine learning engineers and data scientists from getting the best performance from their models. In some cases, it might even dissuade data scientists from using […]

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Amazon and Facebook Collaborate to Optimize Caffe2 for the AWS Cloud

by Joseph Spisak and Yangqing Jia | on | Permalink | Comments |  Share

From Apache MXNet to Torch, there is no shortage of frameworks for deep learners to leverage. The various offerings each excel at different aspects of the deep learning pipeline and each meets different developer needs. The research-centric community tends to gravitate toward frameworks such as Theano, Torch and most recently PyTorch, while many in the […]

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Running BigDL, Deep Learning for Apache Spark, on AWS

by Joseph Spisak, Jason Dai, and Radhika Rangarajan | on | Permalink | Comments |  Share

In recent years, deep learning has significantly improved several AI applications, such as recommendation engines, voice and speech recognition, and image and video recognition. Many customers process the massive amounts of data that feed these deep neural networks in Apache Spark, only to later feed it into a separate infrastructure to train models using popular […]

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Deep Learning AMI for Ubuntu v1.3_Apr2017 Now Supports Caffe2

by Joseph Spisak | on | Permalink | Comments |  Share

We are excited to announce that the AWS Deep Learning AMI for Ubuntu now supports the newly launched Caffe2 project led by Facebook. AWS is the best and most open place for developers to run deep learning, and the addition of Caffe2 adds yet another choice. To learn more about Caffe2, check out the the […]

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AI Tech Talk: An Overview of AI on the AWS Platform

by Victoria Kouyoumjian | on | Permalink | Comments |  Share

AWS offers a family of intelligent services that provide cloud-native machine learning and deep learning technologies to address your different use cases and needs. For developers looking to add managed AI services to their applications, AWS brings natural language understanding (NLU) and automatic speech recognition (ASR) with Amazon Lex, visual search and image recognition with […]

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AWS AI Blog Month in Review: March 2017

by Derek Young | on | Permalink | Comments |  Share

We’ve just finished another month of AI solutions on the AWS AI Blog. Please take a look at our summaries below and learn, comment, and share. Thanks for reading! NEW POSTS Deploy Deep Learning Models on Amazon ECS In this post, learn how to connect the workflow between the data scientists and DevOps. Using a […]

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Deploy Deep Learning Models on Amazon ECS

by Asif Khan | on | Permalink | Comments |  Share

Artificial intelligence (AI) is the computer science field dedicated to solving cognitive problems commonly associated with human intelligence, such as learning, problem solving, and pattern recognition. Machine learning (ML) and deep learning (DL) are computer science fields derived from the AI discipline. Most ML and DL systems have two distinct parts: training (or learning) and […]

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Amazon at WMT: Improving Machine Translation with User Feedback

by Kellen Sunderland | on | Permalink | Comments |  Share

Machine translation is one of the most exciting and important applications of machine learning.  It’s a widely researched topic, both in the academic community and within major technology companies, such as Amazon.  At Amazon, we use machine translation to do things like document the same products in multiple languages.  This helps us to offer additional […]

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Build Your Own Text-to-Speech Applications with Amazon Polly

by Tomasz Stachlewski | on | Permalink | Comments |  Share

In general, speech synthesis isn’t easy.  You can’t just assume that when an application reads each letter of a sentence the output will make sense. A few common challenges for text-to-speech applications include: Words that are written the same way, but that are pronounced differently: I live in Las Vegas. vs. This presentation broadcasts live […]

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