Hi everyone,

I’ve always found the various “Awesome” lists on GitHub for various tech super helpful. I’ve seen a lot of the great ML resource lists before, but hadn’t found one that looked specifically at ML on mobile. So I took some time to compile materials for an “Awesome Mobile Machine Learning” repo. Also includes some materials for other edge devices.

Anyways, hope this is helpful. Happy to add things I’ve missed, or if you’d like, feel free to open a PR!

https://github.com/fritzlabs/Awesome-Mobile-Machine-Learning

Any recent papers pointing to these ideas?

Activation function itself can be learned instead of fixing it to ReLU, tanh, etc.

https://arxiv.org/pdf/1412.6830.pdf (ICLR 2015).

And how about aggregate (reduction) functions like max-pool, average-pool ? Can they be learned instead of being fixed?

I’m a software engineer and have never used notebooks, ever, to write software. Nonetheless my programming style is iterative, granular, and experimental – not unlike workflows I’ve seen used in notebooks. My problem with notebooks is that they’re absolutely terrible for traditional code reuse. I mean, clicking cells and pressing Shift-Enter is technically reusing code, but having a human manually kicking a program along its course, block by block, doesn’t feel like the software I grew up on.

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So I’m curious how you use notebooks, if at all, and at what point, if such a point exists, do you move code from a notebook into a Python script or module. How do you feel about the whole thing? Happy, sad, utterly indifferent?

Maybe you have already heard of Google’s Coral Dev Board with Edge TPU and ask yourself how well it performs. We made a video to share our experience: https://youtu.be/bOYWx1jJCZo

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In the video, we tested an object detection live stream under the following conditions:

– a pretrained MobileNet v2 model, trained on the common objects in context (coco) dataset

– a bounding boxes threshold of 45% confidence because there were way too many boxes displayed in the default configuration

– a camera connected via USB, not the official camera from Coral

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We used this command to run the object detection server described above:

edgetpu_classify_server –source /dev/video1:YUY2:800×600:24/1 –model path/to/model/mobilenet_ssd_v2_coco_quant_postprocess_edgetpu.tflite –labels path/to/labels/coco_labels.txt –threshold=0.45

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You can find more demos to play around here:

https://coral.withgoogle.com/docs/dev-board/camera/

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We hope this example helps you to get started with your own project!

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If you have any idea, what we could build with it, let us know 🙂

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Paul

I did find some RTX 2080 Ti benchmarks that compare NVLink vs. no NVLink performance, like this one https://technopremium.com/blog/4x-rtx-2080-ti-with-quadro-nvlink-performance-test/

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However, I did not find a benchmark so far comparing PCI-E 8x vs. PCI-E 16x performance and if NVLink could lessen a potential bandwith problem when using PCI-E 8x. Does anybody know about such benchmarks? Or do you have a system like that and could share your experiences?

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Many servers nowadays have single or dual root PCI-E solutions to host 8, 10, or even more GPUs with PCI-E 16x, but for workstation boards you often only have 4 usable PCI-E slots (given dual-slot GPUs) and only 3-4 slots with real PCI-E 16x.

Hi there

I have to write a proposal for a DRL/ML product development. We are not planning new research – just implementing key techniques from recent papers. We intent to hire some ML PhDs and scale up the cloud compute, since the dataset (and the opportunity) are large.

The intended audience are upper management, financial decision makers etc. But the ML part will be reviewed for due-diligence by external ML expert advisors.

My own background is MSc/algorithms/development, but i have been reading up here on papers, and doing some coding.

I believe the decision makers are very pro-AI at this stage, so fingers crossed.

Any good examples will be appreciated.

i’m currently working on a project regarding isolated word recognition using svm model. But i’m struck by the scenario to decide an unknown word that put into the model belongs to my specific list of words or not. In more detail, i have a model that could recognize 5 vietnamese words, and the voice signal of words that aren’t in any of these classes will be classify into class 6 (non-key class), but the real training samples i could get for this class can’t cover all the real cases in real life. So my question is how to efficiently differentiate between this class and the others.