mrubashkin

4 ms·

1. 1.▲
   Mind Reading via TensorFlow Classification of EEG Brain Recordings(svds.com)

   7 points by mrubashkin 9y ago|0 comments
2. 2.▲
   Speed Up DeepDream with TensorFlow(svds.com)

   2 points by mrubashkin 10y ago|0 comments
3. 3.▲
   TensorFlow+Nvidia P100s: Automated Biomedical Image Annotation(news.developer.nvidia.com)

   3 points by mrubashkin 10y ago|0 comments
4. 4.▲
   How to Accelerate Deep Learning with Hardware – DeepDream Benchmark(medium.com)

   2 points by mrubashkin 10y ago|0 comments
5. 5.▲
   by mrubashkin 10y ago

   I agree that it would be more efficient to have more wav files in the github repo, but we kept them minimal to reduce the total file size when cloning the repository. You can find more of the Librispeech data here: http://www.ope
6. 6.▲
   by mrubashkin 10y ago

   Hey Aetherspawn, the current repo does not currently have code for decoding individual fed in .wav files that are not in the train/dev/test sets. We'll polish up our code that simplifies decoding and add it to the repo soon,
7. 7.▲
   by mrubashkin 10y ago

   Hey ReverseCold, I had similar experiences to you when I tried TensorFlow fist ~1 yr ago, but since then, the ease of `pip install tensorflow` in a virtualenv has really made it fast and relatively straightforward to use TensorFlow with CPU
8. 8.▲
   by mrubashkin 10y ago

   Thanks for the comment! I haven't read much about OpenCL: https://en.wikipedia.org/wiki/OpenCL I'm looking forward to learning more :)
9. 9.▲
   Recurrent Neural Networks - A Short TensorFlow Tutorial(github.com)

   194 points by mrubashkin 10y ago|20 comments
10. 10.▲
    The Value of Exploratory Data Analysis (EDA)(svds.com)

    15 points by mrubashkin 10y ago|0 comments
11. 11.▲
    by mrubashkin 10y ago

    Hi Vonnik, thanks for the suggestions of tools for us to try out next and add to our list!
12. 12.▲
    by mrubashkin 10y ago

    Hey Alex, thanks for the comment! Have you tried out image classification with the Tegra X1 yourself by chance??
13. 13.▲
    by mrubashkin 10y ago

    We only used the Pi's CPU, which is adequate for classifying images, but not powerful enough to train a model
14. 14.▲
    by mrubashkin 10y ago

    My colleagues and myself have a blog post discussing how to do streaming video analysis on the Raspberry Pi: https://svds.com/streaming-video-analysis-python/ On the Pi3, our application processes 320X240 images at 10
15. 15.▲
    by mrubashkin 10y ago

    Hi annnd! We tried a few times to train a scaled down model on the Pi3, but got nowhere. We've found that the best strategy is to train on the beefiest hardware you have, then transfer the model and run it on the Pi3 for streaming appl
16. 16.▲
    by mrubashkin 10y ago

    Hi Florin, thanks for the comment! Even with the heatsink (which we install on all of the Pis), we were still having overheating issues. We tried a few other things too to mitigate the problem: 1. Reducing sampling rate for the image recogn
17. 17.▲
    by mrubashkin 10y ago

    Hey Sam this is Matt, thanks for your comment and your help a few months back! And for anyone else reading this, Sam is great at getting back to filed issues about installing tensorflow on a Pi: https://github.com/samjabraha