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Craig Quiter
Works at Clari
Attended ASU
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Craig Quiter

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How fast is transfer to AWS us-east1? Also, how does this compare to DynamoDB? Seems like similar latency, but you have easier scaling as they require adjusting per table throughput
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Wow,@digitalocean is about half the price of AWS for same RAM/CPU +SSD https://www.scalyr.com/cloud/ - http://www.cloudlook.com/8-gb-ram-cloud-servers
Welcome! Use this page to compare cloud server costs. Unlike most cheat sheets, here you can compare across providers, evaluate on-demand vs. reserved / leased options, and sort by cost-effectiveness metrics such as "$ per GB of RAM". Feedback: contact@scalyr.com.
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Also many third-party services are only available on AWS (Heroku, CircleCI). This is quickly changing though and tools like Docker make IAAS even more of a commodity than it already is. Soon, you won't care, or even know, what IAAS you are using, just like you don't care about the hardware they are using on AWS.
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Vacation isn’t a luxury. Neither is daydreaming. Don’t skimp.
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Rob Pike's 5 Rules of Programming. Rule 1. You can't tell where a program is going to spend its time. Bottlenecks occur in surprising places, so don't try to second guess and put in a speed hack until you've proven that's where the bottleneck is. Rule 2. Measure. Don't tune for speed until ...
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Awesome utility for understanding shell commands.
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It might not be expressed with code comments, but every line of code comes with documentation. It's just not immediately visible.
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This is the closest I've seen a machine learning benchmark get to a practical application.
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Motion detection happens at least partially within the eye before even reaching the optic nerve. Awesome discovery by "citizen-science" game Eyewire.
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NVIDIA touts performance on Caffe, a library just a year old today, and already heavily featured at ImageNet 2014 http://image-net.org/challenges/LSVRC/2014/

 http://caffe.berkeleyvision.org.
NVIDIA cuDNN is a GPU-accelerated library of primitives for deep neural networks. It emphasizes performance, ease-of-use, and low memory overhead. NVIDIA cuDNN is designed to be integrated into higher-level machine learning frameworks, such as UC Berkeley’s popular Caffe software.
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Drawing on the work of a clever cadre of academic researchers, the biggest names in tech—including Google, Facebook, Microsoft, and Apple—are embracing a more powerful form of AI known as “deep learning,” using it to improve everything from speech recognition and language translation to computer vision, the ability to identify images without human help.
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Large-scale Video Classification with Convolutional Neural Networks

With the ubiquity of videos on the internet, the development of algorithms that can analyze, summarize, and classify their content is an active field of research. While Convolutional Neural Networks (CNNs, http://goo.gl/PqYGzp) are an effective class of models for understanding image content, their development and use for video classification has been limited by the lack of video datasets that match the scale and variety of existing image datasets.

With Large-scale Video Classification with Convolutional Neural Networks (http://goo.gl/6sOs3C), a paper to be presented at the 2014 Computer Vision and Pattern Recognition conference (http://goo.gl/vY2v9k), researchers from Google and Stanford University collaborate to study the performance of CNNs for large-scale video classification using Sports-1M, a new dataset consisting of ~1 million YouTube videos belonging to a taxonomy of 487 classes of sports. 

Utilizing the information present in single, static frames as well as the complex temporal evolution present in video, the CNN learns features on full frame, low-resolution context streams along with centrally cropped high-resolution fovea streams. In doing so, the research shows that CNN architectures are capable of learning powerful features from video data, even if the provided description does not match the content, or there is significant variation on the frame level.

To support future work in this area, the Sports-1M dataset, consisting of 1,133,158 video URLs which have been annotated automatically with labels, has been made available to the research community at http://goo.gl/8imYDN. To see an example of per-frame classification results overlaid on top of a video, watch the video below.
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Have him in circles
908 people
Tom Bagby's profile photo
Blake Householder's profile photo
Darren Mowat's profile photo
Christopher Martin's profile photo
Jeremi Joslin's profile photo
Matt Cordes's profile photo
Joseph Silva's profile photo
Bill Noble's profile photo
Sr Maverick Mitra's profile photo
Work
Occupation
Entreprenuer
Employment
  • Clari
    DevOps, present
  • Wepolls
    cofounder, 2011 - 2013
  • SpyFu
    Software Engineer, 2007 - 2011
  • Microchip, Velocityscape, SpyFu
Basic Information
Gender
Male
Birthday
May 8
Other names
crizCraig
Craig Quiter's +1's are the things they like, agree with, or want to recommend.
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Best Counter I've been to. Everything's super fresh.
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Awesome! Recommend calling in. Also bring cash for just one sandwich.
Public - 2 years ago
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