AWS DeepLens – Deep learning enabled video camera for developers - AWS

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Learn the basics of deep learning - a machine learning technique that uses neural networks to learn and make predictions - through computer vision projects, tutorials, and real world, hands-on exploration with a physical device. AWS DeepLens lets you run deep learning models locally on the camera to analyze and take action on what it sees.


Scalable multi-node training with TensorFlow Amazon Web Services

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We've heard from customers that scaling TensorFlow training jobs to multiple nodes and GPUs successfully is hard. TensorFlow has distributed training built-in, but it can be difficult to use. Recently, we made optimizations to TensorFlow and Horovod to help AWS customers scale TensorFlow training jobs to multiple nodes and GPUs. With these improvements, any AWS customer can use an AWS Deep Learning AMI to train ResNet-50 on ImageNet in just under 15 minutes. To achieve this, 32 Amazon EC2 instances, each with 8 GPUs, a total 256 GPUs, were harnessed with TensorFlow. All of the required software and tools for this solution ship with the latest Deep Learning AMIs (DLAMIs), so you can try it out yourself. You can train faster, implement your models faster, and get results faster than ever before. This blog post describes our results and shows you how to try out this easier and faster way to run distributed training with TensorFlow. Figure A. ResNet-50 ImageNet model training with the latest optimized TensorFlow with Horovod on a Deep Learning AMI takes 15 minutes on 256 GPUs.


Amazon.com: Artificial Intelligence: AI Technology and Deep Learning Systems Explained (Audible Audio Edition): Christian Farsley, Blake Ledger: Audible Audiobooks

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Talking to Strangers: What We Should Know About the People We Don't Know 3.8 out of 5 stars 458 #1 Best Seller in Communication Reference $0.00 Free with Audible trial Talking to Strangers: What We Should Know About the People We Don't Know


Could AI Be the Future of Fake News and Product Reviews?

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When Hillary Clinton's new book What Happened debuted on Amazon's Web site last month, the response was incredible. So incredible, that of the 1,600 reviews posted on the book's Amazon page in just a few hours, the company soon deleted 900 it suspected of being bogus: written by people who said they loved or hated the book, but had neither purchased nor likely even read it. Fake product reviews--prompted by payola or more nefarious motives--are nothing new, but they are set to become a bigger problem as tricksters find new ways of automating online misinformation campaigns launched to sway public opinion. Amazon has deleted nearly 1,200 reviews of What Happened since it debuted on September 12, according to ReviewMeta, a watchdog site that analyzes consumer feedback for products sold on Amazon.com. ReviewMeta gained some notoriety last year when, after evaluating seven million appraisals across Amazon, it called out the online retailer for allowing "incentivized" reviews by people paid to write five-star product endorsements.


How Alexa Is Learning to Converse More Naturally : Alexa Blogs

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To handle more-natural spoken interactions, Alexa must track references through several rounds of conversation. If, for instance, a customer says, "How far is it to Redmond?" and after the answer follows up by saying, "Find good Indian restaurants there", Alexa should be able to infer that "there" refers to Redmond. We call the task of reference tracking "context carryover," and it's a capability that is currently being phased in to the Alexa experience. At this year's Interspeech, the largest conference on spoken-language understanding, my colleagues and I will present a paper titled "Contextual Slot Carryover for Disparate Schemas," which describes our solution to the problem of slot carryover, a crucial aspect of context carryover. "Domain" describes the type of application -- or "skill" -- that the utterance should invoke; for instance, mapping skills should answer questions about geographic distance.